Tributary control of physical heterogeneity and biological diversity at river confluences
Bibliographic record
Abstract
Investigations utilizing a one-dimensional sediment routing model demonstrate that moderate inputs of water and sediment at tributary junctions greatly increase physical heterogeneity in the recipient channel. Simulated physical heterogeneity is most sensitive to the ratios of tributary to mainstream bed load flux and bed load grain size and is less sensitive to relative discharge. Within the model, aggradation drives the processes that augment habitat variability, and in general, any aggradational confluence will be associated with elevated physical diversity. Model output reveals elevated physical diversity at two scales: between distinctive upstream and downstream zones separated by a confluence step and within each zone as a function of local environmental gradients. Total diversity increases as tributary sediment load and caliber increase relative to the mainstream. The ecological implications of the patterns and magnitude of tributary-induced physical heterogeneity are considered, and testable hypotheses are presented. Results highlight the need to accurately characterise patterns of sediment production, delivery, and routing in order to predict local tributary impacts and thereby understand patterns of habitat diversity and biodiversity at network scales.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".