Small biased body size of salmon fry preyed upon by piscivorous fish in riverine and marine habitats
Bibliographic record
Abstract
Predator–prey interactions must be considered when aiming to enhance populations by releasing artificially reared individuals into natural environments. Released individuals create an abundance of prey for predators, and a basic hypothesis of predator–prey interactions suggests that predators select large prey due to the high caloric content. An alternative hypothesis is that small individuals are vulnerable to predation due to their poor predator avoidance. This study tested these hypotheses using stocked chum (Oncorhynchus keta) and masu (Oncorhynchus masou) salmon fry and piscivorous salmonids in marine and riverine habitats in Hokkaido, Japan. Stomach contents were sampled from predators, and fork length of prey fry was measured. Then, their fork length was compared with whole stocked fry (range of mean fork length (±SD): 45.8 ± 2.55 to 49.2 ± 2.76 mm) for each habitat. As a result, prey fry were ∼3%–6% smaller than whole stocked fry, even under a prey-abundant condition (i.e., just after hatchery reared salmon fry were stocked). Piscivorous salmonids pursue schooling fry, and small fry may be easily caught due to their slow speed in avoiding predators.
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 | 0.000 |
| 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".