Effects of turbidity on prey consumption and growth in brook trout and implications for bioenergetics modeling
Bibliographic record
Abstract
Brook trout (Salvelinus fontinalis) were held in an artificial stream to observe the influence of turbidity on mean daily consumption and specific growth rates. Treatment turbidity levels ranged from clear (<3.0 nephelometric turbidity units (NTU)) to very turbid water (> 40 NTU). Observed mean daily specific consumption rates were standardized to the mean weight of all brook trout tested. Turbidity had no significant effect on mean daily consumption, but specific growth rates decreased significantly as turbidity increased. Brook trout in turbid water became more active and switched foraging strategies from drift feeding to active searching. This switch was energetically costly and resulted in lower specific growth rates in turbid water as compared with clear water. Bioenergetics simulations were run to compare observed growth with that predicted by the model. Observed growth values fell below those predicted by the model and the difference increased as turbidity increased. Abiotic factors, such as turbidity, which bring about changes in the activity rates of fish, can have implications for the accuracy of predicted growth by bioenergetics models.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".