A continuum of life history tactics in a brown trout (<i>Salmo trutta</i>) population
Bibliographic record
Abstract
Life history tactics of the brown trout (Salmo trutta) population of the Oir River (Normandy, France) were studied using passive integrated transponder (PIT) tagging data of five consecutive cohorts (5900 individuals) monitored between 1995 and 2002. Results demonstrate that (i) life history traits vary among cohorts, chiefly caused by environmental variability, (ii) juvenile growth, particularly second-year growth, plays an important role in the determination of the growing environment and trout exhibit variable migratory behaviour (from remaining in the natal brook to migrating in the sea) related to their juvenile growth rate, and (iii) the description of life history tactics (including juvenile growth, fine-scale migratory behaviour, and reproduction) can be clarified. Tactics are expressed along a continuum in time (age to reproduce) and space (distance of migration). Flexible life history tactics varying with juvenile growth is consistent with previous studies, but the use of empiric data on growth and migration from PIT tagging allows refining the description of life history tactics, taking into account their continuous distribution in time and space.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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".