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A Bourdon tube is a curved, hollow elastic element that converts a pressure difference into movement of its free end. A common form has an oval tube cross-section and a roughly C-shaped centreline. Fluid pressure is applied through the fixed end, while the other end is closed and free to move. WIKA describes the cross-section tending toward a more circular shape under internal pressure and the resulting end motion being transmitted to a pointer.^[ref]

In a Bourdon tube pressure gauge, the tube, linkage, gearing and pointer form a mechanical measuring chain. The sensing element undergoes elastic deformation; the movement converts a small displacement into a readable measurement indication. A steady pressure reading does not require continuous flow through the closed tube.

For an original small-range model, let free-end displacement be $x=K{p}_g$, with gauge pressure $p_g$ and stipulated $K=0.004\mathrm{mm}\mathrm{kP}{\mathrm{a}}^{-1}$. Let the linkage produce pointer angle $\theta =Gx$ with $G=500{}^{\circ }\mathrm{m}{\mathrm{m}}^{-1}$. At $p_g=100\mathrm{kPa}$,The combined response is $2{}^{\circ }\mathrm{kP}{\mathrm{a}}^{-1}$. These are illustrative coefficients, not a specification for a WIKA instrument. The actual response depends on tube geometry, material and movement.

The reference pressure belongs in the interpretation. With the outside of the element exposed to local atmosphere, the response measures gauge pressure, rather than absolute pressure. WIKA separately describes sealed reference arrangements for absolute-pressure instruments.^[ref]

For instrument calibration, connect the gauge and a suitable measurement standard to a common pressure source, allow equilibration, and compare several increasing and decreasing pressures within the intended range. Record the relative height of fluid-filled pressure ports because hydrostatic pressure can add a difference between them. Differences between increasing and decreasing readings can reveal hysteresis or backlash; a zero shift after loading can reveal a changed mechanical state. The original linear model alone does not establish the gauge's measurement uncertainty.

Double substitution weighing determines the difference between an unknown mass and a reference through repeated substitutions on the same comparator, with a small known sensitivity mass used to determine response under load. NIST's SOP4 compares standard and unknown twice, rather than relying on the balance to establish their entire masses from its zero.^[ref]

For the source's sequence A, let S denote the standard, X the unknown and sw the added sensitivity mass. With optional tare masses omitted, the four pan loads areproducing measurement indications $O_1,O_2,O_3,O_4$. The standard remains the reference; the small addition probes transducer sensitivity close to the actual working load.^[ref]

An original simplified model neglects buoyancy, tare differences and drift, and assumes a common linear response coefficient $a$. Write $\Delta m=m_X-m_S$ and $m_{\mathrm{sw}}$ for the added mass. Relative to the standard load, the readings areThe two comparison differences estimate the same quantity; averaging them gives $D=[(O_2-O_1)+(O_3-O_4)]/2=a\Delta m$. The sensitivity step is $O_3-O_2=a{m}_{\mathrm{sw}}$, soFor original $m_{\mathrm{sw}}=10\mathrm{mg}$ and readings $(100,106,126,120)$, this gives $\Delta m=3\mathrm{mg}$. The offset $b$ and the unknown response coefficient cancel under the model.

Practical weighing records timing and environmental conditions, keeps settings fixed through the comparison, and controls handling and equilibration. The full NIST procedure supplies buoyancy corrections, uncertainty contributions and a comparison using a check standard.^[ref] For equally spaced readings, a linear additive drift cancels from $D$, but can still affect the sensitivity step $O_3-O_2$. Neither the simplified equation nor the method's name guarantees immunity to drift, contamination, nonlinear response or uncertainty in the measurement standard.

A measurement standard realizes a specified quantity with an assigned value and associated measurement uncertainty, and is used as a reference. It can be a physical artifact, a measuring system or a suitable reference material. The value and its measurement uncertainty apply under stated conditions and at the relevant time; a nominal label alone is insufficient.^[ref]

For an original comparison, take a reference resistor with assigned electrical resistance $R_s=100.000\Omega$ and standard uncertainty $u(R_s)=0.001\Omega$. A comparison measures a working resistor's ratio $q=R_x/R_s=1.00020$, with independent $u(q)=0.00001$. The measurement model givesand first-order propagation of uncertainty givesThe reference's measurement uncertainty remains in the result even if the ratio could be read with arbitrarily many digits.^[ref]

A practical comparison records reference conditions, connecting arrangements and repeated readings while checking effects such as instrumental drift. A primary measurement standard has a particular basis for its assigned value; a working reference need not be primary to transfer useful metrological traceability. Distinguish this physical reference from a written technical specification, which is another use of the word standard.

Measurement trueness describes how closely the mean of indefinitely many replicate measured values would agree with a reference value. It concerns systematic error, rather than the random spread described by measurement precision. The VIM treats measurement trueness as qualitative: a numerical error or bias estimate is not itself a numerical value of trueness.^[ref]

An original measurement model separates the two issues:Here $b$ is a fixed offset from the stipulated reference and $\epsilon$ is random measurement error. Thus $\mathbb{E}[X]-x_{\mathrm{ref}}=b$, while the standard deviation of individual readings is $\sigma$. For $n$ independent random variables following this same model, averaging givesMore readings reduce the random scatter of the mean without removing $b$.

For a stipulated length example, instrument A has $b=0.40\mathrm{mm}$ and $\sigma =0.10\mathrm{mm}$; instrument B has $b=0$ and $\sigma =0.50\mathrm{mm}$. A is more precise, while B's limiting mean agrees better with the reference. With 25 independent readings each, the means have standard deviations $0.02\mathrm{mm}$ and $0.10\mathrm{mm}$ respectively, and their expected offsets remain $0.40\mathrm{mm}$ and zero. These are chosen model parameters, not experimental determinations.

Experimentally, compare repeated readings of a stable measurement standard with its assigned value under specified conditions. A finite observed mean difference estimates an offset; its interpretation includes sampling variation and the reference's measurement uncertainty. Determine an appropriate correction through measurement calibration, apply it in the measurement model, and check it with further reference measurements. Neither a small sample spread nor a single reading equal to the reference establishes measurement trueness.

Metrological traceability is a property of a measurement result: a documented, unbroken sequence of measurement calibrations relates it to a stated reference, and every link contributes to its measurement uncertainty. The reference can be a practical realization of a measurement unit, a measurement procedure or a measurement standard.^[ref]

Consider an original mass-measurement chain: a reference measurement standard calibrates a working mass, which is used for instrument calibration of a balance, which weighs an object. Suppose the reference contributes $2\mathrm{mg}$ of standard uncertainty, the transfer adds an independent $3\mathrm{mg}$, and the final weighing adds an independent $4\mathrm{mg}$. With unit sensitivity to these additive contributions,The intermediate working mass has $\sqrt{13}\mathrm{mg}$ of standard uncertainty. That value already includes the reference contribution; adding the same $2\mathrm{mg}$ again at the final step would double-count it. Correlated contributions require covariance terms rather than the independent-input calculation.^[ref]

Documentation must identify the reference, comparison procedures, dates, applicable conditions and uncertainty contributions. A certificate or instrument label alone does not supply the complete chain for every later result. Metrological traceability also does not ensure that the resulting measurement uncertainty is small enough for the intended use.

A primary measurement standard is a measurement standard established by a primary reference measurement procedure, or an artifact selected by convention. A primary reference measurement procedure obtains its result without calibration against a standard of the same kind of quantity; it may still require measurements of other kinds of quantity.^[ref]

For an original ideal pressure realization, independently established force $F=10.000\mathrm{N}$ acts on an effective piston area $A=1.0000\times 10^{-4}{\mathrm{m}}^2$. The measurement model givesIf the inputs have independent standard uncertainties $u(F)=0.010\mathrm{N}$ and $u(A)=2.0\times 10^{-7}{\mathrm{m}}^2$, first-order propagation of uncertainty givesso $u(p)\simeq 224\mathrm{Pa}$. This is an illustrative model, not a complete piston-gauge uncertainty budget: a physical realization must also account for effects such as piston deformation and friction.^[ref]

The essential distinction is how the pressure value is established. Merely comparing a gauge with another pressure gauge does not make it a primary measurement standard. Primary status also does not mean zero measurement uncertainty.

A primary reference measurement procedure determines a result without calibration against a measurement standard for a quantity of the same kind. It can still depend on calibrated measurements of other quantities. The VIM example determines delivered liquid volume from a mass difference and liquid mass density, rather than comparing it with another calibrated delivered volume.^[ref]

For an original ideal calculation, let the receiving vessel's tare mass be $m_t=12.000\mathrm{g}$ and its gross mass after delivery be $m_g=20.000\mathrm{g}$. Suppose the liquid's mass density at the measurement conditions is $\rho =0.8000\mathrm{g}\mathrm{m}{\mathrm{L}}^{-1}$. ThenWith independent standard uncertainties $u(m_t)=u(m_g)=0.001\mathrm{g}$ and $u(\rho )=0.0004\mathrm{g}\mathrm{m}{\mathrm{L}}^{-1}$,so $u(V)\simeq 0.0053\mathrm{mL}$. These are stipulated values, not a recommended liquid or an actual calibration result.^[ref]

The physical procedure would weigh the receiver before and after delivery, establish the liquid temperature and density, and include significant corrections in its measurement model. Evaporation, buoyancy and correlated mass readings can invalidate the simplified expression above. Primary describes the reference relation, not freedom from measurement uncertainty or the absence of any instrument calibration. Its primary status is therefore a claim about the complete procedure, not about this equation alone.

A reproducibility condition of measurement belongs to a declared set of conditions used to assess measurement reproducibility. The VIM set includes different locations, operators and measuring systems, with replicate measurements on the same or similar objects. The systems may also use different measurement procedures; the specification should identify both changed and unchanged conditions.^[ref]

For an original physical comparison, laboratories A and B measure the same stable reference block's length at a stated reference temperature. Each laboratory uses its own operator and differential length comparator. Record the block identity, temperature observations, applied corrections, procedure version and instrument reference in each result. Measurements before and after transport help distinguish reference changes from laboratory differences.

Suppose the stipulated readings, in millimetres, areThe laboratory means are $50.001$ and $50.011\mathrm{mm}$, differing by $10\mu \mathrm{m}$. Each pair has sample standard deviation $\sqrt{2}\mu \mathrm{m}$. Small within-laboratory scatter therefore does not remove the difference between laboratory means.

Because location, operator and system changed together, this comparison alone cannot identify which caused the difference. In a chosen additive measurement model,a $10\mu \mathrm{m}$ difference could be assigned entirely to a system contribution or entirely to a location contribution, with the same predicted means. Separating those explanations needs additional comparisons that vary their combinations.

The conditions describe the experiment's scope; they are not a numerical precision result by themselves. Report the complete measurand and what remained common, including any shared measurement standard. A common reference error can affect both laboratories, so agreement under changed conditions does not by itself establish measurement trueness.

A sensitivity mass is a small calibrated mass added to a loaded weighing instrument to determine its response to a known mass increment. NIST's double-substitution procedure calls this a sensitivity weight and uses it to establish sensitivity under the conditions of the comparison.^[ref] It supplies a local input step rather than a new determination of the entire load from zero.

Let an original comparator give indications $r_0$ before and $r_1$ after adding mass $m_{\mathrm{sw}}$. For a stable linear response over that increment,where $\Delta r$ is another indication difference near that working load. A constant additive zero cancels from each difference; instrumental drift or load-dependent response need not cancel.

For original indications $r_0=860$, $r_1=885$ and assigned sensitivity mass $10.020\mathrm{mg}$, the local response is about $2.495$ indication increments per milligram. A difference $\Delta r=7.5$ increments gives $\Delta m=3.006\mathrm{mg}$. Using only a nominal $10\mathrm{mg}$ label instead would give $3.000\mathrm{mg}$. The assigned mass and its measurement uncertainty, rather than the nominal label alone, enter the comparison.

The added step must be large enough to resolve reliably but remain within the response region relevant to the unknown/reference comparison. NIST specifies selection criteria and regularly spaced observations; it also requires the standards to have valid calibration information.^[ref] Keep handling, settling and environmental conditions consistent. A sensitivity mass does not by itself cancel buoyancy, temperature effects or uncertainty in the main measurement standard. Those contributions remain in the full double substitution weighing model.

A substitution measurement method compares an unknown with a reference by placing them successively in the same relevant position of a measuring system. The comparison seeks a match or measures a difference while retaining as much of the system response as possible. The VIM lists substitution among measurement methods; the detailed sequence belongs to the measurement procedure.^[ref]

For a physical application, NIST's double substitution weighing procedure compares a standard and an unknown of the same nominal mass twice, using a weighing instrument as a comparator. It also determines response with a known sensitivity mass and includes measurement-assurance checks.^[ref] The measurement standard supplies the reference value; the comparator need not establish the whole mass independently from its own zero.

An original simplified drift model shows why sequence matters. Let a comparator indication at equally spaced time index $t$ bewith fixed response coefficient $a$, additive offset $b$ and linear instrumental drift $d$. Observe standard S at $t=0$, unknown X at $t=1,2$, and S again at $t=3$. The mean X indication and mean S indication have the same mean time $3/2$, givingBoth the constant offset and the stipulated linear drift cancel. For $m_S=m_0=100\mathrm{g}$, $a=500{\mathrm{g}}^{-1}$, $b=10$ and $d=2$, an unknown $m_X=100.004\mathrm{g}$ gives indications $10,14,16,16$. Their paired means differ by two, recovering $0.004\mathrm{g}$.

This teaching sequence isolates one cancellation mechanism; it is not the full NIST procedure. Nonlinear drift, unequal timing, changing response or different positioning can defeat it. NIST's procedure separately addresses buoyancy and uncertainty contributions.^[ref] A close match does not remove the reference's measurement uncertainty or establish that the substitution left every relevant condition unchanged.

Ted Doiron, identified as Theodore D. Doiron in NIST's official publication record, is a dimensional metrology researcher and coauthor with John S. Beers of The Gauge Block Handbook by Ted Doiron and John S. Beers. The preface uses Ted Doiron and attributes substantial changes to the handbook's measurement-assurance program to the author abbreviated TD.^[ref]

The scientific challenge is to obtain a reproducible length from a material artifact under changing environmental and measurement conditions. Thermal expansion is a particularly clear example. For a small temperature change,where $\alpha$ is the linear expansion coefficient. An original example with $L(T_0)=100\mathrm{mm}$, stipulated $\alpha =11.5\times 10^{-6}{\mathrm{K}}^{-1}$ and $T-T_0=0.1\mathrm{K}$ gives a length increase $0.115\mu \mathrm{m}$. A small temperature difference can therefore matter when comparing precision gauge blocks.

Even a shared temperature does not cancel every thermal effect. For two equal nominal lengths with different expansion coefficients, the first-order differential change isUsing $L=100\mathrm{mm}$, coefficient difference $1\times 10^{-6}{\mathrm{K}}^{-1}$ and common offset $0.2\mathrm{K}$ gives $20\mathrm{nm}$. This gauge-block thermal mismatch survives a simple subtraction even though both blocks have reached the same temperature. A temperature difference between the blocks introduces another contribution.

The handbook treats temperature corrections and their control alongside comparison procedures and measurement assurance.^[ref] A useful experiment compares the corrected result over controlled environmental changes and checks an independent measurement standard, rather than equating a stable display with an accurate physical length. The examples above are illustrative calculations, not an attributed material dataset or a claim about current NIST instrument performance.

Tie-line endpoint covariance records how the measurement errors of the two measured liquid-liquid tie line endpoints vary together. Using independent mole fraction coordinates, represent the two endpoint estimates by column vectors $\mathbf{a},\mathbf{b}$. Their cross-covariance matrix isAn individual off-diagonal block need not be symmetric; the full joint covariance matrix must be a symmetric positive semidefinite matrix. The uncertainties of the two endpoint vectors alone do not determine their cross-covariance matrix.

Near an exactly balanced, interior tie-line phase-fraction fit, let $\mathbf{g}$ be the row-vector sensitivity coefficient defined in tie-line phase-fraction sensitivity. If the overall composition measurement $\mathbf{y}$ is independent of both endpoints, first-order uncertainty propagation givesThe cross term has a positive coefficient because the two endpoint perturbations enter the phase fraction with the same sign. Correlated endpoints can therefore increase the result. The general rule is to propagate the full joint covariance matrix, rather than assume that correlation always increases or always reduces uncertainty.^[ref]

For a stipulated binary mixture example, use $a=0.20$, $b=0.80$, $z=0.44$, so $\lambda =0.40$. Treat $z$ as exact and give each endpoint standard uncertainty $s=0.006$ with correlation coefficient $\rho$. The lever rule yieldsThis gives $0.002$ for $\rho =-1$, about $0.00721$ for $\rho =0$, and $0.010$ for $\rho =1$. These are first-order standard uncertainties under the stipulated joint error model, not bounds on all possible errors.

A shared calibration error affecting the overall composition as well changes the calculation. Suppose the same additive error $c$ affects $a,b,z$ identically. ThenThe shared offset cancels exactly. More generally, a common affine map $x\mapsto mx+c$ with $m\ne 0$ also cancels in this binary ratio. This common affine calibration cancellation in the lever rule concerns only a truly common contribution; separate sample errors remain. If the shared offset has standard uncertainty $0.006$, incorrectly treating its three appearances as independent would assign a spurious contribution $0.01\sqrt{1+0.6^2+0.4^2}\approx 0.01233$ instead of zero.

In general, stack the inputs as $\mathbf{x}=(\mathbf{y},\mathbf{a},\mathbf{b})$ with joint covariance matrix $C$. Let $J$ be the corresponding Jacobian matrix row from tie-line phase-fraction sensitivity, including its residual correction when needed. Then $u^2(\lambda )\approx JC{J}^T$ automatically includes correlations with the overall composition. The measurement guide specifically identifies common instruments, measurement standards and reference data as sources of such correlations.^[ref]

An instructive measurement design analyzes the two equilibrium phases and the overall mixture using one instrument calibration, then repeats the entire procedure with independent instrument calibrations. The within-run contrasts and between-run changes probe different error contributions. Repeated readings under one unchanged instrument calibration cannot by themselves establish the uncertainty of its shared offset. The measurement model must identify which influences are common before the joint covariance matrix can represent them.