Canopy cover and anurans: nutrients are the most important predictor of growth and development
Bibliographic record
Abstract
Bottom-up effects such as the availability of light and nutrients can have large impacts on primary producer quantity and quality, which is then translated into the growth and development of consumers. The use of “canopy cover” as a bottom-up predictive factor is a broad categorization, as canopy cover controls both the amount of light allowed into a pond and the nutrient load through leaf litter. To test how light and nutrients influence pond ecosystems, we manipulated inorganic nutrients and light in a 2 × 3 full-factorial, large-scale mesocosm experiment. Larval American Bullfrogs (Lithobates catesbeianus (Shaw, 1802)) were reared for 6 weeks at low densities and then assessed for development, growth, and survival at the end of the experiment. We also collected weekly samples of potential food resources (phytoplankton and periphyton) for the estimation of algal production and stoichiometric quality (carbon:nitrogen:phosphorus). Light had strong effects on food resource quality; however, resource quality did not significantly predict tadpole growth or development. Instead, nutrients seemed to be the most important factor as a stimulator of total algal primary production and some unknown pathway, which in turn affected tadpole development. Ours is the first study to investigate canopy cover using a comprehensive causal model, and our results suggest in regards to tadpole growth and development, canopy cover is important mainly as a source of nutrients to ponds.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".