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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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 teacher head, 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".