Two social communities in the Pearl River Estuary population of Indo-Pacific humpback dolphins (<i>Sousa chinensis</i>)
Bibliographic record
Abstract
The way human activities impact animal populations can depend on social structure, which is important to understand in social species such as cetaceans. We investigated association patterns in Indo-Pacific humpback dolphins ( Sousa chinensis (Osbeck, 1765)) inhabiting the Pearl River Estuary near Lantau Island, Hong Kong, using a 10-year data set for 88 individuals. Our analyses revealed two social communities. Each had its own region of core use, to the north and to the west of the island, but their overall ranges partially overlapped northwest of Lantau. The northern community had a fission–fusion structure characterized by short-term associations, while the western community had more long-term associations. Mixed-community groups included calves more often than exclusive groups, so between-community associations may arise from common habitat usage, by females especially, in the overlap area. Recent range extensions by the northern community into the west are likely a response to habitat destruction north of Lantau. This suggests ease of movement between the north and the west is necessary for northern-community dolphins to access suitable habitat, and gives new concern to construction projects planned for the region. We emphasize our study as an example of how sociobiological information can be important in understanding human impacts on animal populations.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".