Association patterns of bottlenose dolphins (<i>Tursiops aduncus</i>) off Point Lookout, Queensland, Australia
Bibliographic record
Abstract
The social structure of animal communities is usually measured through interactions or associations of individuals within the community. However, investigating and identifying association patterns for large communities of social animals can be difficult, given the logistical difficulties of identifying a large number of individuals within a given area and time period. In this study, over 550 individuals were identified within a large community of bottlenose dolphins (Tursiops aduncus) sampled intensively during the winters of 1998 and 1999 off Point Lookout, Queensland, Australia. Association patterns within this community were analysed using the half-weight index of association, including seven criteria for selecting individuals for inclusion in the analysis. Selection criteria were based on the number of times an individual was sighted during the entire study period. Overall, the community showed a highly fluid association pattern, with only two selection criteria showing association patterns that differed significantly from random. This type of association pattern is commonly reported for large communities of cetaceans. However, without the inclusion of other population information such as estimates of the proportion of identifiable individuals in the community and of community size, it appears that association patterns for these large communities may not be accurately assessed.
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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 | 0.001 | 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".