Influence of turbidity, food density and parasites on the ingestion and growth of larval rainbow smelt Osmerus mordax in an estuarine turbidity maximum
Bibliographic record
Abstract
We investigated the impact of turbidity, food density and parasites on ingestion and growth rates of rainbow smelt larvae Osmerus mordax. These 3 variables were selected because of their potential to substantially ~nfluence the feeding success, growth, and the subsequent survival of smelt larvae. A laboratory experiment was first performed to evaluate, in turbulent conditions, the combined effects of turbidity and food density on the ingestion and growth rates of smelt larvae. A f~eld survey of the gut contents of larval smelt was conducted to directly estlmate ingestion rates in 2 different regions of the St. Lawrence estuarine turbidity maximum (ETM) exhibiting different levels of turbidity but otherwise sharing similar environmental conditions. This study demonstrated that lower energetic costs are incurred by larvae that exploit similar feeding conditions at higher turbidities. Larval rainbow smelt in the ETM fed during the coincidence of dayllght hours and flooding tide. Cestode parasites (genus Protocephalus) were found in the digestive tract of 38% of the larvae collected in the ETM. Parasitised larvae ingested half as much food as non-parasitised larvae. The decrease in feeding due to parasitism was associated with a reduced growth rate as suggested by the sign~ficantly lower standard lengths observed in parasitised larvae. Moreover, the size advantage of non-parasitlsed larvae is expected to be ampMied because larger larvae ingest proportionally more food than smaller larvae. We suggest that the impact of parasitism on larval survival and subsequent recruitment m flshes merits far more attention than afforded to date.
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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 | 0.000 |
| 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".