Bibliographic record
Abstract
The mutual acceptance of acoustical calibrations and measurements between industrial countries is essential for international trade and the removal of no-tariff trade barriers. For example, the sound level or sound power emitted by a machine measured at the country of manufacture with certified instruments and methods in accordance with international standards, should be acceptable by the importing country without the requirement to duplicate the measurements. To achieve this mutual recognition, it is necessary for the exporting country to have proven capabilities via international comparisons and an unbroken chain of traceability from their national metrology institute to the machine shop level. Under the umbrella of the Bureau International does Poids et Mesures (BIPM), the Consultative Committee on Acoustics, Ultrasound and Vibration (CCAUV) has arranged international calibration comparisons, involving over 15 countries. The above comparisons require a lot of effort from each participating country. One may ask the question: Who is the beneficiary of international comparisons? The detailed answer is rather complex. In general, the results of International Calibration Comparisons provide confidence in the measurement capabilities of the participants. In the long term, the consumer is the ultimate beneficiary.
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 | 0.168 | 0.258 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.017 | 0.035 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.012 | 0.008 |
| Insufficient payload (model declined to judge) | 0.018 | 0.005 |
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".