Measurement Uncertainty in Fire Tests— A Fire Laboratory Point of View
Bibliographic record
Abstract
Since the adoption of ISO/IEC 17025, testing laboratories have been required to perform Measurement Uncertainty analysis for the tests within their scope. Four points of recurring debate are discussed: (1) The variability in fire test results due to unforeseen/uncontrolled variables is generally far greater than the measurement uncertainty of the result. (2) It is important not to confuse “measurement uncertainty” (MU) with “precision” of results. MU has a very specific meaning as used in ISO/IEC 17025, ISO/IEC Guide 98-3 Guide to the Expression of Uncertainty in Measurement (GUM) and ISO Guide 99 International vocabulary of metrology—Basic and general concepts and associated terms (VIM). (3) An uncertainty result is not used to justify passing or failing a product with results very near the pass/fail limit. Where the measured result is subject to a measurement uncertainty evaluation and reporting, compliance limits may or may not require extending the test result by the MU value in making a compliance determination. (4) ISO/IEC 17025 specifically exempts standards that specify limits on sources of uncertainty and specify the form of reporting from a required MU statement. This makes uncertainty estimates inapplicable to those fire tests.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".