Effects of multiple predator species on green treefrog (<i>Hyla cinerea</i>) tadpoles
Bibliographic record
Abstract
Prey species that occur across a range of habitats may be exposed to variable communities of multiple predator species across habitats. Predicting the combined effects of multiple predators can be complex. Many experiments evaluating the effects of multiple predators on prey confound either variation in predator density with predator identity or variation in relative predator frequency with overall predation rates. We develop a new experimental design of factorial predator combinations that maintains a constant expected predation rate, under the null hypothesis of additive predator effects. We implement this design to evaluate the combined effects of three predator species (bass, aeshnid and libellulid odonate naiads) on mortality rate of a prey species, Hyla cinerea (Schneider, 1799) tadpoles, that occurs across a range of aquatic habitats. Two predator treatments (libellulid and aeshnid + libellulid) resulted in lower tadpole mortality than any of the other predator treatments. Variation in tadpole mortality across treatments was not related to coarse variation in microhabitat use, but was likely due to intraguild predation, which occurred in all predator treatments. Hyla cinerea tadpoles have constant, low survival values when exposed to many different combinations of predator species, and predation rate probably increases linearly with predator density.
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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) | 0.001 | 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".