Bibliographic record
Abstract
For metrology to be recognized as a measurement science, it must be seen to be using the scientific method. This requires metrologists to make predictions that can be tested and validated by experiment. The fundamental testable prediction is usually a variant of ‘agreement within the claimed uncertainty’, and the experiment is usually a comparison of two or more nominally identical measurements. The normalized error, E n , can be generalized as the ratio of {a difference of the two values} to {the standard uncertainty in the difference of the two values}. This definition can apply equally to the difference between a particular measurement and a reference value with an uncertainty or to an unmediated bilateral difference between two measurements considered as peers. This latter interpretation leads to the creation of a family of bilateral E n values in a comparison, which can be aggregated by taking the root-mean-square (RMS) average. This RMS E n is a norm that can support intuitive ideas of ordering performance in the comparison. The mean-square E n is a chi-squared-like statistic and can be evaluated by Monte Carlo simulation to perform quantitative tests of the ideal agreement hypothesis for a comparison. The use of these statistics in broader aggregates is discussed: averaging across similar unlinked key/regional comparisons, across a range of artefact values, across different principal measurement techniques in a given metrology area or even across all major metrology areas spanning the entire International System of Units. Each of these ‘averages’ can be done as an overall aggregate of all participants or can focus on one particular participant's RMS E n aggregated with respect to all its peers' results.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.070 | 0.055 |
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".