Borders and Barriers: challenges of Fisheries Management and Conservation in Open Systems
Bibliographic record
Abstract
Abstract Large rivers often bisect geopolitical boundaries where management goals may be at odds for a shared fishery, creating fragmented management zones. Fragmentation due to physical barriers may further impact the fishery by reducing fish passage. Our goal was to estimate basin‐wide parameters (i.e. movement, survival and capture probabilities) of a large‐river species known to move throughout watersheds. We tagged13 892 Channel Catfish in the Red River of the North (Red River) and Lake Winnipeg in Manitoba, Canada, and collected 553 recaptures. We estimated 2.2% of catfish are moving from the Red River to Lake Winnipeg each month and 9.4%, primarily large (>600 mm) individuals, moved upstream through a dam (monthly). Approximately 5.6% of catfish moved to the USA each month, and only one fish returned. Our results suggest the lower reaches of the Red River may be a source population for the USA, where survival is lower, and Lake Winnipeg. The complex movements of Channel Catfish throughout the Red River, across barriers and international boundaries, suggest conservation and management of fish populations should be watershed wide. Copyright © 2017 John Wiley & Sons, Ltd.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".