Influence of substrate orientation on tadpoles feeding efficiency
Bibliographic record
Abstract
In nature, tadpoles encounter food on substrates oriented at different angles (e.g. vertically along stems, horizontally on the bottom of the pond). We manipulated the orientation of food-covered surfaces to test how different orientations of surfaces affect tadpoles' feeding efficiency. We studied taxa that differed in the oral morphology of their larvae and position in the water column. We hypothesized that species would differ in their ability to graze upon surfaces at different orientations and that differences in the tadpoles' feeding ability would result in different growth rates. The orientation of food-covered surfaces did not affect the growth rate of bottom-dwelling tadpoles (whose growth rate varied only between species). Among midwater tadpoles, some species appear to have a generalist strategy and experienced a high relative growth rate on numerous substrate orientations, whereas others achieved high growth rates only on flat substrates (i.e. at 0° and 180°). We conclude that oral morphology constrains tadpoles' ability to feed at different substrate orientations, and this could lead to niche partitioning in structurally complex aquatic environments. Because physical parameters of the environment can affect tadpoles' growth rate, characterizing these features might help us better understand how competition structures tadpole assemblages.
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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 | 0.001 | 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".