Habitat use of Guiana dolphin Sotalia guianensis in a heavily urbanized embayment
Bibliographic record
Abstract
Understanding environmental and anthropogenic variables that influence the presence of dolphins in coastal areas is fundamental for conservation planning and forecasting. In this study, we applied generalized additive models to identify areas of high probability of occurrence of Guiana dolphins in Mucuripe embayment, a heavily urbanized area in northeastern Brazil. Sighting and effort data were collected during systematic, boat-based surveys between 2009 and 2011. Models were built using 70% of the data to test the model and 30% to evaluate its predictive performance. Variables investigated included depth, slope, seabed complexity, and distance to the coast, breakwaters, and the fishing grounds. The best model explained 40.8% of the total deviance. Seabed complexity, distance to the breakwaters and distance to the fishing grounds were the most important variables, with dolphins showing a preference for areas with a less complex seabed immediately adjacent to the breakwaters (<500 m, decreasing with distance) as well as a preference for fishing grounds (again decreasing with distance). Using the validation data, the model showed excellent performance. The habitat use and preference of Guiana dolphins in the study area seem to be mainly influenced by foraging opportunities, with dolphins concentrating in areas with higher prey abundance and where foraging success is higher because of a strategy called ‘barrier-feeding’, in which animals herd fish against piers, breakwaters, and the coast.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".