Bibliographic record
Abstract
At Inter-Noise 91 in Sydney, Australia, the International Institute of Noise Control Engineering (I-INCE) Board took the first steps to enlarge the scope of the General Assembly’s activities. In particular, the need for technical initiatives to address noise issues of international interest was recognized. Such issues usually involve important policy matters, but the focus is on technical details. The following year in Toronto, Canada, the Board authorized studies on two topics, upper noise limits in working environments and the effect of regulations on road vehicle noise, to be carried out by the General Assembly. Two further initiatives, community noise and noise barriers, were approved in subsequent years. To date, the Technical Initiatives Program has produced two final reports and one draft report. At the 1999 General Assembly in Fort Lauderdale, FL, four new technical initiatives were approved, noise policies and regulations, noise control for schoolrooms, noise from outdoor recreational activities, and noise labels for products. A fifth initiative was added by the General Assembly in Nice, France, on the topic of noise as a global policy issue. This work will give an overview of the objectives and process of the I-INCE Technical Initiatives Program.
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 | 0.026 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 0.007 |
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".