Modeling the effect of boat traffic on the fluctuation of humpback whale singing activity in the Abrolhos National Marine Park, Brazil
Bibliographic record
Abstract
Since the moratorium on whaling, the Brazilian government and local Non-Govermental Organizations (NGOs) have adopted and encouraged a more sustainable use of whales as tourist attractions. Nevertheless, concerns about boat traffic impacts on whale population health have arisen, especially in protected areas such as marine parks. The Abrolhos Marine National Park is the seasonal habitat for the breeding population of humpback whales in the Western South Atlantic. We acoustically monitored 7% of the park area during 26 days using marine autonomous recording units and evaluated the responses of whales to boat traffic by measuring changes in male singing activity. The recorded humpback whale songs were analyzed to locate and count individual singers. We modeled the fluctuation in the number of singers over time in response to: number of acoustic boat events, tide height, lunar phase, hour of the day, the quadratic function of hour of day, day of the season, and presence of light. Generalized linear models were used to fit the singer count data into a Poisson distribution and log link. We found an important negative effect of boat traffic on singing activity. There is evidence that the interaction between phases of the moon and the quadratic function of hour of day also affect singing behavior. Adaptive management should aim at reducing the number of noise events per boat, which can improve the whale watching experience and reduce the impact on male singing behavior.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".