Morphometric Conversions for 33 Shark Species from the Western North Atlantic Ocean
Bibliographic record
Abstract
such as size-at-age and size-at-maturity, as essential inputs (Maunder and Punt, 2013), while multiple data-limited assessment approaches estimate fshing mortality from changes in the length distribution (Chong et al., 2020).Further, assessing progress relative to management regulations often requires the ability to accurately convert among length measurements, such as precaudal, standard, fork, natural total, and stretched total lengths, due to inconsistencies in the types of standard measurements collected among various research programs (Francis, 2006).For example, straight-line fork length (FL SL ), which is measured as the straight-line distance from the tip of the snout to the fork of the tail, is the measurement type designated for shark regulations in the Atlantic Ocean by both the International Commission for the Conservation of Atlantic Tunas (ICCAT) and by NOAA's National Marine Fisheries Service Highly Migratory Species Division 1 (ICCAT, 2016).However, particularly for large fsh, many researchers and fshermen use over-the-body (OTB; synonymous with curved measurements for this paper) measurements in which a measuring tape is laid along the surface 1 https://media.f isheries.noaa.
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.
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 | 0.004 | 0.003 |
| 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) | 0.002 | 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".