Impacts of artificial rearing on cisco <i>Coregonus artedi</i> morphology, including pugheadedness
Bibliographic record
Abstract
Cisco ( Coregonus artedi Lesueur, 1818) in the Laurentian Great Lakes declined throughout the 19th and 20th centuries. Managers are attempting to restore Great Lakes cisco and other coregonines using multiple approaches, including stocking. A potential obstacle to these efforts is that artificially reared coregonines can display deformities and morphological differences compared to wild fish, but the impacts of artificial rearing on cisco morphology are not well understood. We compared morphologies of wild cisco to their artificially reared offspring, including one family that was exposed to three rearing temperature treatments. We found that artificially reared cisco had smaller eyes, shallower bodies, fewer gill rakers, and longer paired fins than their wild parents. We also found that artificially reared cisco were pugheaded, and this result held for another cisco population and rearing facility. Across the temperature treatments we tested, rearing temperatures did not impact the degree of pugheadedness or other morphological differences. Our results have important implications for coregonine restoration efforts. Future work should evaluate whether morphological differences that arise through artificial rearing affect cisco fitness in the wild.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".