Development and Evaluation of a Standard Weight (Ws) Equation for Yellow Perch
Bibliographic record
Abstract
Abstract.—Weight-length data for 78 populations of yellow perch Perca flavescens in 20 states and 6 Canadian provinces were used to develop a standard weight (Ws) equation. We used the regression-line-percentile (RLP) technique, which provides a 75-percentile standard, to develop the Ws relationship. The proposed equation in metric units is \\og\\QW5 = —5.386 + 3.230 logioL; Ws is weight in grams and L is total length in millimeters. The English equivalent of this equation is logic ws =-3.506 + 3.230 logic/-; ws is weight in pounds and L is total length in inches. These equations are proposed for use with 100-mm (4-in) and longer yellow perch. Relative weight (Wr) values calculated with the proposed Ws equation did not consistently increase or decrease with increasing fish length. Mean population Wr values were significantly correlated with growth and size structure of yellow perch populations, but correlation coefficients were generally low. Wege and Anderson (1978) proposed that rel-ative weight (\\Vr) be used as an index offish con-dition. Relative weight values are obtained by di-viding the actual weight of a fish by a standard weight (Ws) for a fish of that length and multiplying
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".