An Updated Combined Biomass index of abundance for North Atlantic Swordfish Stock 1963-2012
Bibliographic record
Abstract
Surplus Production Models of North Atlantic swordfish have been used in addition to age \nstructured virtual population analyses by ICCAT's SCRS to evaluate the status of the resource \nand to provide a basis for management advice. Production models require a standardized index \nof relative abundance in terms of biomass. The standardized biomass index of abundance \ndeveloped for the 2006, 2008 and 2012 ICCAT SCRS meetings for North Atlantic swordfish was \nrevised and updated with data through 2015. Generalized Linear Modeling (GLM) procedures \nwere used to standardize swordfish catch (biomass) and effort (number of hooks) data from the \nmajor longline fleets operating in the North Atlantic; United States, EU-Spain, Canada, Japan, \nMorocco and EU-Portugal. As in past analyses, main effects included: year, area, quarter, a \nnation-operation variable accounting for gear and operational differences thought to influence \nswordfish catchability, and a target variable to account for trips where fishing operations \nvaried according to the main target species. Interactions among main factors were also \nevaluated.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".