Hydrologic regime and turbidity influence entrance of terrestrial material into river food webs
Bibliographic record
Abstract
We used stable isotope signatures of deuterium (δD) and a Bayesian stable isotope mixing model to estimate contributions of algae versus terrestrial plants to consumers during different hydrologic phases in three Texas rivers spanning a gradient of turbidity and light penetration. In the two rivers where high-flow pulses increased turbidity, assimilation of source material by consumers varied according to discharge stage. In these rivers, algae made greater contributions to macroinvertebrates and fish biomass following low-flow periods, and terrestrial plants made greater contributions following high-flow pulses. In the river with greatest loads of suspended sediments, contributions of material from terrestrial plants also increased slightly following an extended low-flow period, possibly because of increased abundance of inedible cyanobacteria. During flow pulses, lower algal biomass and production, combined with increased inputs of terrestrial organic matter from watersheds and riparian habitats, can result in greater inputs of terrestrial material into aquatic food chains. These patterns most closely match predictions of the River Wave Concept, which posits that flow is the key process determining the source of organic matter assimilated by higher consumers in rivers. Incorporation of interactions between hydrology and turbidity into river ecosystem models should facilitate more accurate predictions of food web dynamics.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".