Relationship between background invertebrate drift concentration and flow over natural flow recession and prediction with a drift transport model
Bibliographic record
Abstract
This study advances understanding of the flow dependency of invertebrate drift in rivers and its relevance to drift-feeding fish. Background drift concentration varied spatially and with flow over natural flow recession (lower mid-range to low flow) in a reach of a New Zealand river, largely consistent with passive entrainment. Seven taxonomic groups (dominated by Leptophlebiidae and Chironomidae) exhibited positive drift concentration–flow relationships, and one (sandy/stony-cased caddisflies (Conoesucidae)) exhibited negative relationships. A mechanistic drift transport model accurately predicted the slope, but not y intercept, of the drift concentration–flow relationship for the total drift community that positively responded to flow but performed more poorly at the taxon or size-class level. Partitioning the relative influence of drift entry and dilution revealed that positive drift concentration–flow relationships arose from entry overwhelming dilution with increasing flow. Drift transport models have potential for predicting relative (%) effects of flow change on concentration and rate of drift-prone invertebrates. This paves the way for drift transport models to inform inputs to net rate of energy intake models for drift-feeding fish.
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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) | 0.001 | 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 | 0.001 |
| 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".