Bibliographic record
Abstract
Fish show a tremendous diversity in patterns of reproductive investment and in associated breeding systems (i.e. parental care and sexual selection, including the number of mates obtained by both sexes and the manner in which they are obtained through competition for mates and resources, courtship, and mate choice). These patterns play an integral role in shaping the evolution of populations and their dynamics, and thus changes in these patterns necessarily affect population viability. Artificial culture of fish in hatcheries, net‐pens and gene banks almost invariably disrupts the natural breeding system and alters fitness‐related traits. The implications, both genetic and ecological, of the intentional and unintentional release of these fish for wild populations are largely dependent on what occurs during breeding and its subsequent effects on offspring performance. Our findings and those of others have indicated that gene flow from cultured to wild populations is frequently impeded by altered breeding behaviour and biased by sex and life history strategy. Moreover, breeding affects subsequent offspring performance through not only genetic (e.g., disruption of co‐adapted gene complexes, MHC non‐assortative mating), but also non‐genetic maternal effects (e.g., breeding time and location, egg size). While significant advances have been made in the last decade, our understanding of the reproductive ecology of cultured fish in the wild remains somewhat in its infancy. Such study continues to be integral in enlightening our management of cultured fishes in the wild, and more broadly for increasing our understanding of fish breeding systems and thus population dynamics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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) | 0.001 | 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".