Science and the management of underwater noise: Information gaps and polluter power
Bibliographic record
Abstract
To regulate underwater noise rationally and efficiently, we need to know its impact on population and community biology. This link can rarely be studied directly because of logistics and the spatial and temporal scales of ecological processes. There are two principal approaches: correlational studies of noise levels with population events or measures; and experiments in which the response variables (usually short-term behavioral measures) are poor proxies for the population and ecosystem parameters about which we are concerned. Experimental studies also have costs. These may include the introduction of additional noise, delay of substantive regulation, and, when polluters are major funders, perceived gagging or biasing of knowledgeable scientists. This is a particular problem with underwater noise because the U.S. Navy (a major noise polluter) and allied organizations fund so much acoustic research. Consequently (a) managers must recognize that underwater noise is dangerous but its most important consequences cannot currently be determined; (b) following the precautionary approach, noise levels should be reduced, sources distanced from marine life, and new noises avoided; (c) correlational studies are generally preferred to experimental ones; (d) major noise polluters should not directly fund the research, instead providing fees to independent bodies which commission research and recommend regulations.
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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 | 0.032 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.017 | 0.026 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".