Hatchery-induced morphological variation in an endangered fish: a challenge for hatchery-based recovery efforts
Bibliographic record
Abstract
Recovery plans for many endangered fishes include production of large numbers of individuals in hatcheries to augment wild populations. However, effects of hatchery culture on phenotypic development are poorly documented for most species. June sucker (Chasmistes liorus) is an endangered lake sucker endemic to Utah Lake (Utah, USA), and recovery plans include raising thousands of juveniles to stock in the lake. To determine effects of hatchery culture on morphology of young June sucker, we raised individuals from four different families in both lake and hatchery environments and compared shape variation among families and between environments. We compared shape from three perspectives: lateral body, ventral mouth, and lateral head views. Mean shape varied between hatchery-reared and lake-reared individuals in each of the three views. In addition, hatchery-reared individuals exhibited higher variance in shape both within and among families compared with shape variance in lake-reared individuals. Hatchery-reared individuals exhibited divergent phenotypes compared with lake-reared individuals. These results suggest that exposure to the native environment during early life stages may be important for phenotypic development suitable to native habitats. Hatcheries should incorporate designs and practices to minimize divergent phenotypic development of individuals.
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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 | 0.002 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".