Investigating the spatiotemporal variation of fish choruses to help identify important foraging habitat for Indo-Pacific humpback dolphins, Sousa chinensis
Bibliographic record
Abstract
Abstract Given the common physical overlapping between coastal developments and important marine mammal habitats, there is a need to identify potentially important foraging grounds for dolphins when informing marine spatial planning and management of underwater noise. Hydrophones were deployed at four locations either side of the mainland China–Hong Kong Special Administrative Region border to monitor the presence of soniferous fishes; a key prey item for Indo-Pacific humpback dolphins. Five distinct chorus-types were identified; each showing spatiotemporal variability. Each chorus-type was assumed to represent a separate species. Chorus-type diversity also differed between sites, with SP4 and SP5 types only being detected within Hong Kong waters where bottom trawling is illegal. Chorus-type SP1 was only detected at the recording sites in mainland Chinese waters. Call rates and chorus duration were highest during the spring and summer months. Given these dolphins show a predator-prey relationship, these data provide new information on the local fish communities at a much finer-scale than fish landing records and a baseline of fish activity in an environment that is challenging to explore. Overlaid with acoustic detections of foraging dolphins, these data form a basis for identifying potentially important foraging habitats that should be afforded the highest priority for protection.
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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 | 0.000 |
| 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".