Impact of sediment and nutrient inputs on growth and survival of tadpoles of the Western Toad
Bibliographic record
Abstract
Summary 1. Sediment and nutrient loading in freshwater systems are leading causes of aquatic habitat degradation globally. We investigated the impacts of fine‐sediment and nutrient additions on the growth and survival of western toad (Bufo boreas) tadpoles and emergent metamorphs in mesocosm and exclosure experiments. 2. Mesocosm tanks received weekly pulses of fine sediments to create initial concentrations of 0, 130 and 260 mg L−1 of suspended sediment and either bi‐weekly additions of nutrients (N = 160 μg L−1, P = 10 μg L−1) or no additions in a factorial design. Within mesocosms, tadpole exclosures allowed for quantification of tadpole grazing pressure on periphyton biomass, chlorophyll‐a and sediment deposition. 3. Tadpoles receiving sediment additions experienced slower growth rates and reduced survival to metamorphosis, although no effects of treatment were detected on size at metamorphosis or time to metamorphosis. Nutrient additions also lowered survival, but had no impact on other measured parameters of tadpole fitness. Dissections and gut content analysis revealed that tadpoles ingested sediment in large quantities altering the proportion of the organic content of ingested food. 4. Together these results suggest that although sediment was readily consumed by tadpoles, its presence in the larval environment had an overall negative effect on tadpole growth and survival, although not as severe as predicted.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".