Use of a modified form factor to compare condition among North American lake sturgeon stocks
Bibliographic record
Abstract
In fisheries management it is often useful to compare length and weight relationships or condition among populations across a species’ range. Currently, the most commonly used metric for this is relative weight (Wr), although some problems have arisen with the use of Wr including the impact of seasonal changes in body condition due to reproduction, and length-related biases in standard weight equations. We propose the use of a modified form factor (mFF) based on the regression of log10α vs β (weight–length model parameters) within a species, to provide a quick and meaningful comparison of mean condition among North American lake sturgeon populations. We used the α and β parameters from 63 lake sturgeon weight–length models from 43 lake sturgeon populations from throughout their range in the equation to calculate the mFF for the 63 samples. Modified form factor values of juvenile, adult male, and female lake sturgeon from the Winnebago System, Wisconsin, in various stages of reproductive development had a 98.0% correlation with their respective relative condition values over a wide range of mFF values. Simple t-tests on sets of mFF values can be used to test the condition differences between populations or sub-samples within populations. Lake sturgeon from the Winnebago System, Wisconsin, USA were found to show W–L relationships best described in two stanzas: all juveniles <71.1 cm, and juveniles and adults combined, but separate by sex, ≥71.1 cm. Likelihood ratio tests found significant differences between male and female (>71 cm) W–L models; juvenile (≤71 cm) and male (>71 cm) models; and juvenile (≤71 cm) and female (>71 cm) models.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".