Red snapper (<i>Lutjanus campechanus</i>) abundance on oil and gas platforms based on mark–recapture methods in the northern Gulf of Mexico
Bibliographic record
Abstract
Oil and gas platforms provide reef habitat for many fish species on continental shelves. Red snapper ( Lutjanus campechanus) are an important component of these communities in the Gulf of Mexico, but abundance estimates are difficult to obtain. Hydroacoustic and visual-video surveys have been applied in previous abundance estimates, but such methods have difficulties. To improve abundance and fishing mortality ( F) estimates, mark–recapture methods were applied to red snapper at 22 platforms in the northern Gulf of Mexico from February 2017 through May 2020. Estimates were adjusted for emigration, tagging mortality, natural mortality, fisher nonreporting, and tag retention. Mean ± SE abundance·platform–1 (563 ± 107, range = 109–1407) and F (0.36, range = 0–1.25·platform–1) were not significantly affected by year, location, depth, or distance-from-shore. Based on an estimated 904 platforms, there were 508 952 (900 845 kg) red snapper on platforms. This indicated that red snapper on platforms accounted for only 1.2% of the 2016 Gulf of Mexico red snapper biomass. Thus, required removal of platforms will not significantly affect the red snapper stock in this area.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".