Unintentional selection, unanticipated insights: introductions, stocking and the evolutionary ecology of fishes
Bibliographic record
Abstract
Natural environmental change has produced countless opportunities for species to disperse into and persist in habitats where they previously did not exist. Introduction and stocking programmes have facilitated similar sorts of colonization opportunities across considerably greater geographical scales and often in much shorter periods of time. Even though the mechanism of colonization differs, the result can be the same: evolutionary change in the colonizing population in response to novel selection pressures. As a consequence, some human-mediated fish transfers have unintentionally yielded novel research opportunities to study how phenotypes and genes interact with their environment and affect ecological and evolutionary change. The primary purpose here is to explore how work, directly or indirectly involved with human-mediated transfers, has unintentionally yielded novel research and research opportunities in fish ecology and evolution. Insights have produced new knowledge or altered previously held perceptions on topics such as local adaptation, rate of evolutionary change, phenotypic plasticity, alternative reproductive strategies, population structure and colonization probability. Well-documented stocking programmes, especially in terms of history, numbers and original population sources, can provide highly fertile ground for generating further insights on the ecology and evolution of fishes and of the factors likely to influence the success of conservation-based, restoration programmes.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".