Rock crevice morphology and forest contexts drive microhabitat preferences in the Green Salamander (<i>Aneides</i> <i>aeneus</i>)
Bibliographic record
Abstract
Little is known about how vertical rock habitats are selected by organisms in forest ecosystems. Multiple lungless salamanders use rock outcrops in the Appalachian Mountains of eastern North America, with Green Salamanders (Aneides aeneus (Cope and Packard, 1881)) showing the strongest associations as an outcrop specialist. Although previous work has identified environmental correlates of rock face and arboreal habitat use in this species, it remains to be known if and how Green Salamanders select crevice refugia as a function of both outcrop morphology and the context of outcrops within the surrounding forest. We performed an intensive survey of an abundant Green Salamander population on Virginia’s Appalachian Plateau to examine which features of vertical habitats are associated with salamander occupancy. Occupancy was highest in deeper rock crevices closer to surrounding trees, a likely consequence of arboreal behavior and the ability for crevice refugia to modulate the surrounding microclimate. Although uncertainty exists with regards to the generality of these results across the species’ range, our results underscore linkages between embedded rock outcrop habitats and their surrounding forest contexts for amphibians. Our results also provide a model of Green Salamander habitat associations that may be valuable to efforts to elucidate its geographic distribution.
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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 | 0.001 | 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".