Debris dams as habitat for aquatic invertebrates in forested headwater streams: a large-scale field experiment
Bibliographic record
Abstract
To evaluate the effects of debris dams on aquatic invertebrate communities, we sampled benthic invertebrates in debris dams and riffles in three forested headwater streams in New Zealand. As part of a large-scale field experiment, debris dams were subsequently removed from three treatment sections in each of the streams to assess effects on invertebrate communities. Prior to debris dam removal, total invertebrate densities in debris dams were not significantly different from those in riffles. However, densities of Ephemeroptera, Plecoptera and Trichoptera taxa were significantly higher in debris dams than in riffles. Debris dams contained a higher number of less common taxa (defined as <1% of total catch) and significantly higher densities of shredders. Densities for Coleoptera, Diptera and Trichoptera taxa were significantly higher in the autumn than in the spring. Non-metric multidimensional scaling axis scores indicated that both habitat and season had a significant effect on aquatic invertebrate community composition. At the reach scale, the effects of debris dam removal on the aquatic invertebrate communities were not statistically detectable because debris dams comprised only a small proportion of total habitat. However, these data highlight the importance of debris dams in contributing to the diversity of aquatic invertebrates in forested headwater streams.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".