Effects of muscle lipid concentration on wild and hatchery brown trout (<i>Salmo trutta</i>) smolt migration
Bibliographic record
Abstract
Annually, hatchery programs are releasing millions of salmonid smolts into the Baltic Sea. Recent estimations indicate a decline in smolt sea survival, questioning the ecological and socioeconomic values of these programs. Concurrently, hatchery smolts have increased in lipid concentration. Salmonids display partial migration, and it is suggested that the ratio of migrants/residents is affected by individual smolt energetic status. To test whether the increased energetic status of hatchery smolts could explain the noted decrease in survival, we released wild trout smolts, conventional hatchery smolts, and hatchery smolts of low energetic status into a Baltic Sea river. Using telemetry, we obtained data on the number of successful migrants, their swimming speed, and diel migratory behaviour. A much lower proportion of conventional smolts (30%) successfully migrated to the coast. No difference was found between wild (74%) and hatchery smolts of low energetic status (64%). Furthermore, conventional smolts migrated slower and showed no diel migratory pattern. The results are of high relevance for hatchery programs producing partially migrating fish.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".