When Do Mountain Whitefish (Prosopium williamsoni) Spawn? A Comparison of Estimates Based on Gonadosomatic Indices and Spawner and Egg Counts
Bibliographic record
Abstract
Determining when fish spawn has major implications for effective fisheries management, particularly in dam-controlled rivers where reproductive potential may be affected by an altered hydrograph. Three methods for estimating spawn timing in riverine broadcast spawners were compared for their precision, effort and potential impact on a population of Mountain Whitefish in the regulated Lower Duncan River, Canada. The first method is based on the Gonadosomatic Index (GSI), which is a measure of the relative mass of an individual’s gonads. The second method is based on counts of aggregating adults, while the third method is based on passive egg collection using egg mats. Analysis of the GSI data provided the most precise estimates. It estimated that spawning occurred between October 30th and November 26th in 2010 and between November 8th and November 27th in 2011. Collection of GSI data required moderate effort and had some impact due to the need for lethal harvest. Analysis of the spawner counts using a simple Bayesian Area-Under-the-Curve model provided less precise estimates of spawn timing but the method likely had negligible impact on the population and required only moderate effort. Deployment of egg mats required high effort and collected insufficient information to derive statistical estimates of spawn timing. We discuss how information from different methods could be combined together into a single integrated model to maximize the precision while minimizing the effort and impact.
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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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".