Evaluating Alberta’s Fall Index Netting Protocol for Assessing Northern Pike (Esox lucius) Populations
Bibliographic record
Abstract
To assess the validity of Fall Index Netting (FIN) for monitoring Northern Pike (Esox lucius) populations, we tested five hypotheses relating FIN catches to independently measured abundance and sizes of Northern Pike, including testing the economic effectiveness of FIN monitoring. These tests used fisheries survey data on 133 lakes collected between 2000 and 2020. Mark-recapture studies and angler surveys were paired with FIN surveys done in the same year. We found that 1) FIN catch rates were related to Northern Pike population density, 2) FIN catchability was unrelated to Northern Pike density, 3) FIN catch size distributions were predictably related to Northern Pike sizes, 4) FIN catch rates were related to angler catch rates, and 5) FIN monitoring was economically efficient. Continued assessments of this monitoring technique are necessary to enhance statistical accuracy, improve stakeholder trust, and continue to provide environmental benefits for fish stocks, fisheries managers, and stakeholders. Keywords: Northern Pike, Index Net, Catchability, Size Vulnerability, Relative Abundance
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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 | 0.022 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".