Bibliographic record
Abstract
More than a decade has passed since the Convention for Trade in Endangered Species (CITES) first alerted the United Nations Food and Agriculture Organization (FAO) of the urgent need to establish programs for collecting biological and trade data and for managing the impacts of fishing on shark populations.By 2000, FAO had developed the International Plan of Action for the Conservation and Management of Sharks (IPOA-Sharks) to form part of the Code of Conduct for Responsible Fisheries.Under the IPOA-Sharks, each of more than 80 signatory nations is obliged to develop and implement a National Plan of Action for the Conservation and Management of Sharks, where the term 'shark' covers all chondrichthyan groups (sharks, batoids, and holocephalans) (Anon. 2000).The IPOA-Sharks emphasises that the harvest of chondrichthyan fishes should be biologically sustainable, economically rational, utilising all body parts of the sharks killed, and managed to ensure biodiversity conservation and maintenance of ecosystem structure and function.However, despite the best intentions for better management by many nations, fishing for these species continues to increase in response to the evergrowing demand for shark meat and other products from these animals.Shark fins, for example, are among the most highly priced fisheries products in eastern Asia and this is stimulating the targeting of sharks and retention of only their fins, the practice known as 'finning'.Direct fishing mortality is not the only impact on chondrichthyan populations.There are fishing impacts on habitats through disturbance of biotic communities and substrates.Shipping and underwater exploration, construction, mining, and electrical installation also affect habitats, and increasing ambient sound, light, electromagnetic fields, and chemical contamination stimulate the sensory systems of chondrichthyan fishes.Understanding and measuring responses to these impacts and others such as from invasive species and perhaps aquaculture pose enormous research challenges.An overlay to complicate the research difficulties addressing these impacts is the uncertain magnitude of climate change, the effects of which have the potential to obscure the effects of fishing and other anthropogenic activities.Research will have to be an important component of developing appropriate management responses to address many of these impacts.Innovative approaches are required for collecting
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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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.012 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".