Status of aboriginal, commercial and recreational inland fisheries in <scp>N</scp>orth <scp>A</scp>merica: past, present and future
Bibliographic record
Abstract
Abstract The inland fisheries of North America (i.e. Canada and the United States of America) are diverse in terms of the sectors that harvest fish, the waters fished and the species targeted. Aboriginal fisheries have a long tradition of harvesting fish for food and ceremonial purposes using gears such as dip nets and spears, and targeting species such as suckers (Catostomidae) and upriver migrating salmon (Salmonidae). The commercial sector includes large‐scale industrial operations on the Great Lakes and Mississippi River as well as smaller‐scale fisheries throughout North America that harvest fish for food or the bait industry. The recreational fishery is the largest sector (millions of participants) and includes everything from specialised catch‐and‐release fisheries for muskellunge, Esox masquinongy Mitchill and black bass (Micropterus spp.) to put‐and‐take fisheries for rainbow trout, Oncorhynchus mykiss (Walbaum). All sectors provide substantial socio‐economic benefit and regionally can have significant cultural value and yield an important amount of food protein. Using the best available information and a number of assumptions, total harvest for all three sectors in the inland waters of North America was estimated to be >480 000 t yr−1. Nonetheless, there are a number of internal threats that face these fisheries including over‐exploitation, bycatch/release mortality as well as external threats such as inter‐sectoral conflict, environmental change, water availability, invasive species and habitat alteration. Given that most inland fisheries are managed at the state/provincial level, there is a need to adopt management strategies that are holistic, coordinated and trans‐jurisdictional if inland fisheries in North America are to be sustainable in the future. There is also a critical need for information management systems that enable regional data to be scaled up to the national or continental level, which would facilitate the generation of inland fisheries status reports and the monitoring of trends through time. All stakeholders must recognise that while inland fisheries tend to not receive the same attention from the media, public or politicians as marine fisheries, the potential for local and broad‐scale irreversible changes exist and need to be identified and addressed if the many ecosystem services that inland fisheries provide are to be maintained.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".