Impacts of fisheries on the Critically Endangered humpback dolphin Sousa chinensis population in the eastern Taiwan Strait
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Biological and fisheries data were analysed to assess the impact of fisheries mortality on a Critically Endangered subpopulation of <100 humpback dolphins Sousa chinensis in the eastern Taiwan Strait (ETS). Substantial interactions between ETS S. chinensis (hereafter Sousa) and fishing gear are known to cause dolphin mortality. In 2009, a total of 6318 motorised fishing vessels were operating from ports within Sousa habitats. An average of 32 fishing craft per kilometre was observed along a 200 km stretch of Sousa habitat. Based on a photo-identification catalogue, >30% of the ETS Sousa subpopulation exhibited injuries caused by fishing gear. Three individuals were photographed with fishing gear attached to their bodies, and 1 dolphin was found dead with fresh injuries caused by fishing gear. To ensure recovery of ETS Sousa, mortality due to human causes should be reduced to <1 individual every 7 yr. Fisheries bycatch is the most serious threat to these dolphins and needs to be eliminated as soon as possible to avoid extinction. Preventing the use of trammel nets, other gillnets and trawling throughout their habitat would be the single most effective conservation measure for ETS Sousa in the short term. Other fishing methods are available, and using the most selective, sustainable fishing methods available will benefit not only dolphins but also fish stocks, seabirds and other species, as well as the fishing industry, which depends on these species for its long-term viability. However, in the short term, there are costs associated with switching to more selective fishing gear.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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 it