Development and Evaluation of Condition Indices for the Lake Whitefish
Bibliographic record
Abstract
Abstract Despite frequent use of length-based condition indices by fisheries managers and scientists to describe the overall well-being of fish, these indices are rarely evaluated to determine how well they correlate with more direct measures of physiological or ecological condition. We evaluated common condition indices (Fulton's condition factor KF, Le Cren's condition index KLC, and two methods of estimating relative weight Wr) against more direct measures of physiological condition (energy density, percent lipid content, and percent dry mass) and ecological condition (prey availability) for lake whitefish Coregonus clupeaformis in Lake Huron. We developed four standard weight (Ws) equations using the regression length percentile (RLP) method: one for the species as a whole, and three separate equations describing immature, mature male, and mature female lake whitefish from 385 populations in North America. Species RLP-Ws showed less length-related bias and more closely matched empirical quartiles of lake-specific mean weight than did maturity- or sex-specific RLP-Ws equations. Significant length-related bias was detected in EmP-Wr. No biologically significant length-related bias was detected in KLC, but this index was specific to a single population of fish. Species RLP-Wr showed no significant length-related bias, and KF was significantly size dependent. All length-based condition indices were significantly correlated with energy density, percent lipid content, and percent dry mass. The index most strongly correlated with all three measures of physiological condition was KF, likely because both the physiological measures and KF exhibited positive relationships with body size. Across two Lake Huron sites, RLP-Wr was significantly correlated with density of prey (amphipods Diporeia spp.). Of the two condition indices developed in this study, RLP-Wr was consistently more strongly correlated with physiological condition indices than was EmP-Wr.
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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 | 0.001 | 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 | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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 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".