Wasted fishery resources: discarded by‐catch in the USA
Bibliographic record
Abstract
Abstract Fishery by‐catch, especially discarded by‐catch, is a serious problem in the world's oceans. Not only are the stocks of discarded species affected, but entire trophic webs and habitats may be disrupted at the ecosystem level. This paper reviews discarding in the marine fisheries of the USA; however, the type, diversity and regulatory mechanisms of the fisheries are similar to developed fisheries and management programmes throughout the world. We have compiled current estimates of discarded by‐catch for each major marine fishery in the USA using estimates from existing literature, both published and unpublished. We did not re‐estimate discards or discard rates from raw data, nor did we include data on protected species (turtles, mammals and birds) and so this study covers discarded by‐catch of finfish and fishable invertebrates. For some fisheries, additional calculations were required to transform number data into weight data, and typically length and weight composition data were used. Specific data for each fishery are referenced in Harrington et al. ( Wasted Resources: Bycatch and discards in US Fisheries , Oceana, Washington, DC, 2005). Overall, our compiled estimates are that 1.06 million tonnes of fish were discarded and 3.7 million tonnes of fish were landed in USA marine fisheries in 2002. This amounts to a nationwide discard to landings ratio of 0.28, amongst the highest in the world. Regionally, the southeast had the largest discard to landings ratio (0.59), followed closely by the highly migratory species fisheries (0.52) and the northeast fisheries (0.49). The Alaskan and west coast fisheries had the lowest ratios (0.12 and 0.15 respectively). Shrimp fisheries in the southeast were the major contributors to the high discard rate in that region, with discard ratios of 4.56 (Gulf of Mexico) and 2.95 (South Atlantic). By‐catch and discarding is a major component of the impact of fisheries on marine ecosystems. There have been substantial efforts to reduce by‐catch in some fisheries, but broadly based programmes covering all fisheries are needed within the USA and around the world. In response to international agreements to improve fishery management, by‐catch and discard reduction must become a regular part of fishery management planning.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".