An experimental test of sub-hourly changes in macroinvertebrate drift density associated with hydropeaking in a regulated river
Bibliographic record
Abstract
We investigated the response of the invertebrate drift to the regulated peaking Magpie River under experimental sub-hourly changes in flow conditions. Benthic invertebrates were also quantified upstream of invertebrate drift sampling sites in order to examine the propensity of nearby invertebrates to drift under hydropeaking cycles. To provide an understanding of the natural patterns of drift in relation to changes in discharge, the proximate natural Batchawana River was similarly sampled. Increased discharge was associated with higher drift densities and greater particulate organic matter in the Magpie River. At the family level, multiple drift responses were observed suggesting that ecological traits and behaviors drive drift response to changes in discharge. The drift densities of some invertebrates varied in proportion to discharge; whereas, other invertebrates in the drift decreased to pre-peaking levels shortly after peak discharge was reached, with some invertebrates exhibiting a secondary peak in density as discharge returned to pre-peaking levels. Although average drift densities were comparable between the two rivers, drift density on the natural Batchawana River was much more stable than that of the Magpie River. Changes in drift density associated with flow manipulation likely impact feeding patterns and behavior of 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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".