Proposed standard weight (<i>W</i> <i>s</i>) equation and standard length categories for Goldeye
Bibliographic record
Abstract
Abstract Objective Goldeye Hiodon alosoides relative weight (Wr) and proportional size distribution (PSD) have not been able to be evaluated in populations across their range. The objective of this project was to develop and assess standard weight (Ws) equations with three different techniques along with developing standard length categories for Goldeye. Methods Length and weight data for 64,435 Goldeye Hiodon alosoides from 96 populations across Canada and the United States were collected to develop a Ws equation. We developed standard weight equations for Goldeye with the regression-line-percentile (RLP), empirical-percentile linear (EmP-L), and empirical-percentile quadratic (EmP-Q) techniques. Result We propose the RLP equation as log10W = 2.979 × log10L − 4.979, the EmP-L equation as log10W = 3.048 × log10L − 5.151, and the EmP-Q equation as log10W = 1.254 × log10L + 0.365 × (log10L)2 − 2.950, where W is weight (g) and L is total length (TL, mm). We also developed PSD length categories of 13, 20, 26, 33, and 40 cm TL representing stock, quality, preferred, memorable, and trophy categories, respectively. Conclusion These Ws equations and standard length categories will aid fisheries biologists in assessing Goldeye populations holistically across their range when developing management strategies.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".