A Spatially Explicit Assessment of Changes in Chinook Salmon Fisheries in Lakes Michigan and Huron from 1986 to 2011
Bibliographic record
Abstract
Abstract We produced a spatially explicit assessment of the changes in stocking, catch, fishing effort, and catch per effort (CPE) for Chinook Salmon Oncorhynchus tshawytscha in Lakes Michigan and Huron from 1986 to 2011. We focused on describing spatial differences in the changes that occurred during three well-known episodes of rapid change: (1) a decline in abundance during 1986–1994 in Lake Michigan, (2) a recovery in abundance during 1994–2006 in Lake Michigan, and (3) a decline in abundance during 2002–2010 in Lake Huron. We used a spatial grid system to describe and contrast trends in fishing effort and CPE among the main lake basins (Michigan, Huron, and Georgian) and subregions within those basins. We applied linear regressions, ANCOVAs, and Tukey's tests to assess differences. We found that trends differed among and within basins during all three episodes, which resulted in changes in the distribution of fishing effort and CPE. Fishing effort generally decreased in all basins and subregions over the entire 25 years, but it decreased less in areas where CPE had increased. The timing of the recovery episode and second mortality episode overlapped, so CPE simultaneously increased from 79 to 139 fish/1,000 h of fishing in the Michigan basin and decreased from 65 to 35 fish/1,000 h of fishing in the Huron basin. Movement of fishing effort and Chinook Salmon from the Huron basin to the Michigan basin probably occurred during this time. The CPE did not change significantly in the Georgian basin. Within basins, CPE exhibited sharp declines of more than 80% in some subregions during both mortality episodes but declined much less or not at all in others. After the recovery episode, areas of highest CPE in the Michigan basin had shifted from eastern subregions to western subregions. Received February 2, 2016; accepted April 23, 2016 Published online August 30, 2016
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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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".