Wood Frog (<i>Lithobates sylvaticus</i>) skeletogenic plasticity in anthropogenic habitats
Bibliographic record
Abstract
Habitat loss and landscape fragmentation are major causes of numerous amphibian population declines. Although logging activities have been related to serious effects on growth rate and size at metamorphosis in several species, less is known about skeletal developmental modifications associated with disturbed habitats. We studied the effects of forest canopy modifications caused by logging activities on the skeletal development of a pond-breeding anuran, Wood Frog (Lithobates sylvaticus). Biotic and abiotic factors were collected for 30 semi-permanent ponds located in three habitat categories (regenerated forest, along skidding trails, and logged areas). A sample of 58 cleared and double-stained tadpoles were analyzed to compare developmental trajectories among habitats. Water temperature and pond morphometric characteristics, which were correlated with logging-related habitat alteration, had a major impact on tadpole developmental differences among pond categories. Developmental plasticity was evident in both absolute and relative timing of chondrification and ossification between regenerated forest ponds and disturbed ponds (i.e., along skidding trails and in logged areas). Ossification and chondrification patterns had a different response to environmental factors. Notably, we observed the early onset of skeletogenesis in the disturbed ponds, which may result in deleterious effects on the fitness of post-metamorphosed juveniles.
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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) | 0.002 | 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".