Abundance estimation and capture of sablefish (<i>Anoplopoma fimbria</i>) by longline gear
Bibliographic record
Abstract
Longline surveys in Alaska measure sablefish (Anoplopoma fimbria) relative abundance and are the primary information source used for abundance and quota estimation. Hook timer, on-bottom (soak) time, hook density, hook pattern, bait type, and bait condition experiments and mathematical models were used to evaluate the performance of the longline surveys for estimating sablefish relative abundance. The rate that sablefish encountered the longline gear decreased with on-bottom time independently of sablefish density in the sampled area. Sablefish were adept at locating available baits, even when few remained. The decrease in encounter rate appears related to odor concentration at the leading edge of the odor plume. The ability to locate baits, even when few remain, differs from previous models of fish capture by longline in which the probability that a fish located a bait was proportional to the number of available baits. Decreased encounter rate and the ability to locate baits efficiently imply that longline catch rates likely provide an accurate index of fish abundance if the on-bottom time is long enough to cover the period when most fish encounter the gear and the initial bait density is high enough that baits remain available throughout the soak; the weak link between catch rate and abundance is the unknown extent that factors such as temperature and food availability affect the proportion of fish caught.
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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 | 0.001 |
| 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".