Do food quantity and quality affect food webs in streams polluted by acid mine drainage?
Bibliographic record
Abstract
Food influences the structure of consumer communities; however, in polluted streams food resources may be severely reduced and act as an additional stressor. We examined the quantity and quality of basal resources and prey items for invertebrate consumers in 12 streams along an acid mine drainage (AMD) gradient (pH range: 2.7–7.1) and characterised their diets using stable isotope and gut content analyses. Algal and detrital resource quantity (biomass) and quality (C : N ratio) did not differ significantly along the gradient, except algal C : N, which was lower in highly stressed and circumneutral streams. Furthermore, availability, size and diversity of animal prey decreased significantly with increasing stress. Most primary consumers were generalist feeders, but algae became increasingly common in their diets as pH increased. Predators were opportunistic and consumed prey that reflected locally abundant taxa. Generally, these were small-bodied chironomids in highly stressed streams and larger-bodied prey (mayflies, caddisflies, stoneflies) in moderately stressed and circumneutral streams. Our results indicated that acidity and metal toxicity were the primary stressors of communities in streams affected by AMD and that food quantity was unlikely to be limiting for primary consumers. However, food availability may be an additional stressor affecting predators in streams receiving AMD.
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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 | 0.001 | 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".