Reproductive performance of alternative male phenotypes of growth hormone transgenic Atlantic salmon (<i>Salmo salar</i>)
Bibliographic record
Abstract
Growth hormone (GH) transgenic Atlantic salmon (Salmo salar) is one of the first transgenic animals being considered for commercial farming, yet ecological and genetic concerns remain should they enter the wild and interact reproductively with wild fish. Here, we provide the first empirical data reporting on the breeding performance of GH transgenic Atlantic salmon males, including that of an alternative male reproductive phenotype (i.e. small, precocially mature parr), in pair-wise competitive trials within a naturalised stream mesocosm. Wild anadromous (i.e. large, migratory) males outperformed captively reared transgenic counterparts in terms of nest fidelity, quivering frequency and spawn participation. Similarly, despite displaying less aggression, captively reared nontransgenic mature parr were superior competitors to their transgenic counterparts in terms of nest fidelity and spawn participation. Moreover, nontransgenic parr had higher overall fertilisation success than transgenic parr, and their offspring were represented in more spawning trials. Although transgenic males displayed reduced breeding performance relative to nontransgenics, both male reproductive phenotypes demonstrated the ability to participate in natural spawning events and thus have the potential to contribute genes to subsequent generations.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".