How well are velocity effects on ∂<sup>13</sup>C signatures transmitted up the food web from algae to fish?
Bibliographic record
Abstract
Summary 1. Benthic algae fractionate carbon isotopes less at low water velocities because of reduced boundary layer exchange, and this effect on δ13C is passed on to consumers via trophic transfer. This study examines the relationships between δ13C signatures of consumers (invertebrates and salmonid fishes) and water velocity in the Sainte Marguerite River, QC, Canada, and compares them to patterns for periphyton, both along the river main‐stem and in a small tributary. 2. Relationships of δ13C signatures of herbivore/grazers and collector/gatherers with water velocity were strong and similar to those of periphyton, but relationships for filter‐feeders were weak, probably reflecting the effect of spatial averaging of their food supply as a result of downstream transport. 3. Velocity effects on salmonid signatures were much weaker than those of lower trophic levels, being barely significant except in the small tributary where the fish were resident and isolated from the main river. In the river main‐stem, even when reach standardised (reach mean subtracted from each data point), fish signatures were only weakly related to water velocity. 4. The fidelity with which velocity effects are transmitted to consumers from benthic algae is highly variable, and depends on a combination of consumer and resource movements, in addition to the trophic position of the consumer.
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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 | 0.001 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".