Oviposition-site selection by the toad <i>Melanophryniscus rubriventris</i> in an unpredictable environment in Argentina
Bibliographic record
Abstract
Oviposition-site selection plays an important role in the reproductive success of amphibians. In unpredictable environments where resources vary within a season, amphibians should select oviposition sites using parameters that can be easily evaluated, or spawn in several ponds to increase offspring survival. Melanophryniscus rubriventris (Vellard, 1947) uses shallow ephemeral ponds in an unpredictable montane-forest environment. During 40 consecutive days, we surveyed potential spawning sites and measured several biotic and abiotic factors to determine if any of these factors influenced breeding-site selection. We also described the spawning behavior of this species. Water temperature and pond level (flooded or not) were significant predictors of whether a pond was used or not. Warmer ponds would permit accelerated development of larvae in habitats where ponds are ephemeral and their presence unpredictable. Because of the short and unpredictable hydroperiods, it will be better to select a pond full of water instead of evaluating other pond characteristics that can be very variable. Mating pairs spread several egg masses to different sites but in the same pond. This behavior is likely a consequence of pairs avoiding interactions with intruding males and not as a strategy to enhance offspring survival.
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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".