Identifying humpback whale calving grounds: ensemble modelling and spatial analysis of critical habitat in Bahía de Banderas, Mexico
Bibliographic record
Abstract
Abstract Understanding the relationships between cetaceans and their environment is crucial for conservation. This study examined humpback whales in Bahía de Banderas, Mexico, identifying key calving habitats. From 2018 to 2023, 1066 sightings were recorded, including 242 mother–calf groups, 109 mating groups, and 715 other groups. Spatial analysis revealed a non-random distribution; both the Kruskal–Wallis and Wilcoxon–Mann–Whitney tests detected significant differences (P < 0.05) in site preferences. Calving mothers favoured habitats with a mean depth of 59 m and a distance of 2 km from the coast, while mating groups preferred locations at 126 m and 4 km, and other groups chose areas at 149 m and 4 km. All groups were found in relatively flat areas around 2° seafloor slope. A dispersion test indicated a significant relationship between the location of calving mothers and environmental factors. K-means clustering showed 83.6% of calving mothers' sightings at depths less than 40 m and 2 km from the coast. Ensemble species distribution models identified three critical calving areas: one large area (261.8 km2) along the north coast and two smaller areas (9.5 and 5.4 km2) at the southern end of the bay. This study highlights Bahía de Banderas as a vital breeding habitat for humpback whales, providing insights for conservation strategies to protect calving grounds during the breeding season.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".