Ghosts of the deep – Biodiversity, fisheries, and extinction risk of ghost sharks
Bibliographic record
Abstract
Abstract Ghost sharks (subclass Holocephali) remain a largely data‐poor group of cartilaginous fishes. The general paucity of attention may partially be related to identification and unresolved taxonomic issues, occurrence in the deep oceans, and their low value and interest in fisheries (which some notable exceptions). Here, we synthesize and assess the extinction risk of all known extant ghost sharks (52 species) by applying the IUCN Red List of Threatened Species Categories and Criteria. Ghost sharks have a low proportion of threatened (8%) and Near Threatened (8%) species, with most species (69%) assessed as Least Concern. The group still exhibits some data deficiency (15%), and biological information is lacking for most species. Endemism is high, with 37% of species known from only one location or one country. Species richness was highest in the Northeast Atlantic, off the northwest coast of Africa (Morocco to Mauritania), the East China Sea, New Zealand and off the northwest coast of South America (Ecuador and Peru). Ghost sharks are predominately taken as by‐catch, but some targeted fishing and/or retention for the liver oil trade occurs. Species‐specific reporting, monitoring and management are required to assess population trends, and further investigation is needed on trade and use, particularly for higher risk species including the sicklefin chimaeras (genusNeoharriotta) and the American Elephantfish (Callorhynchus callorhynchus, Callorhinidae).
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| 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".