The influence of fluctuating ramping rates on the food web of boreal rivers
Bibliographic record
Abstract
Abstract A BACI (before‐after‐control‐impact) sampling design was applied to determine the possible effects of ramping rate (RR) regulation on food webs structure and function in a regulated boreal river. We used carbon and nitrogen stable isotope signatures of primary producers, macroinvertebrates and fish to determine variations in the source of carbon fuelling the food web as well as changes in the food web structure under variable RR flow regime. We hypothesized that unrestricted RR would (1) increase the connectivity between terrestrial and aquatic environments allowing for a higher contribution of terrestrial carbon to support the food web and (2) decrease food web length because of frequent disturbances. Unrestricted RR had little influence on δ13C values for the overall food web with most of the differences found between impacted sites compared and control sites, indicating that the proportion of various carbon sources entering the diet of consumers remained unchanged under unrestricted RR. In contrast, significantly higher δ15N values were measured in impacted sites (invertebrates and fish) and as well as under unrestricted ramping flow regime (invertebrates). Further, unrestricted RR was associated to a significant decrease in the difference between macroinvertebrates and fish δ15N signatures, equivalent to a reduction of the length of the food web by at least one trophic level. Results from this study indicate that RR should be taken into consideration in the regulation of operating regimes on rivers. Copyright © 2008 John Wiley & Sons, Ltd.
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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 | 0.002 |
| 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 | 0.001 |
| 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".