Does habitat or depth influence catch rates of pelagic species?
Bibliographic record
Abstract
The efficiency of a pelagic longline fishing operation and the species composition of the resulting catch is influenced primarily by the relationship between the distribution of hooks and species vulnerability, with vulnerability described by either depth or some suite of environmental variables. We therefore fitted longline catch rate models to determine whether catch is estimated better by vertically distributing a species by depth or by environmental conditions (e.g., temperature, thermocline gradient, and oxygen concentration). Catch rates were estimated by two methods: (i) monitoring longlines where the vertical distribution of hooks and catch in relation to depth and environmental conditions is known, and (ii) applying a statistical habitat-based standardization (statHBS) model to fishery and environmental data to develop relative abundance indices for bigeye tuna (Thunnus obesus) and blue shark (Prionace glauca). Results indicated that an understanding of gear dynamics and environmental influences are important for analyzing catch-per-unit-effort (CPUE) data correctly. Analyses based on depth-specific catch rates can lead to serious misinterpretation of abundance trends, despite the use of sophisticated statistical techniques (e.g., generalized linear mixed models). This illustrates that inappropriate inclusion or exclusion of important covariates can bias estimates of relative abundance, which may be a common occurrence in CPUE analysis.
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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 | 0.007 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".