Assessment of cumulative effects of underwater sound: A collaborative approach.
Bibliographic record
Abstract
A multidisciplinary team of scientists working under a research agreement between the University of California, Santa Barbara and BP America Production Company has undertaken a 2-year project, currently ongoing, to develop one or more standardized and practical methods for assessing cumulative effects of anthropogenic underwater sound on marine mammals. The work of the team is based on access to existing scientific information without substantial involvement of additional primary research although topics for future research may be part of the eventual recommendations. While the final goal of the project is to conceptualize and specify widely applicable methodologies, case studies are being used to help formulate and test the feasibility of assessment frameworks. The primary case study involves the significant hydrocarbon related industrial activity that took place in the Beaufort Sea (Alaska and Canada) during late summer and autumn 2008 and its potential cumulative effect on the population of bowhead whales (Balaena mysticetus) that dwell and transit through the region in their annual migration cycle. Methods that include advanced numerical modeling of sound propagation, individual-specific dose exposure calculation, and sensitivity analysis of the range of potential responses as a function of various influence factors are applied in the execution of these studies.
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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 | 0.041 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".