Local and Landscape Variables Influencing the use of Ponds by Wood Frogs (Lithobates sylvaticus) in the Shakwak Valley, Yukon
Bibliographic record
Abstract
A global decline in amphibian population numbers has generated a large body of research focused on amphibian habitat selection and species diversity conservation.The purpose of this study was to analyze the significance of both local and landscape-scale variables on wood frog (Lithobates sylvaticus) pond habitat selection in the Shakwak Trench in Yukon, Canada.Presence or absence of wood frogs was used to determine pond occurrence values for 40 different ponds.Independent local variables were collected in the field and through the use of Geographic Information Systems (GIS).Landscape variables were derived with GIS and were analyzed under a 1000m buffer around the perimeter of the study ponds.Pond perimeter and dominant perimeter vegetation were significant local variables.Small ponds with a dominant sedge vegetation types seemed to be selected over larger ponds.Large lake area in the landscape buffers had a significant negative relationship for wood frog habitat selection.Significant variables in this study are similar to those in previous studies or can be linked to other important variables such as pond hydroperiod and total forested area.Results should be considered to act as preliminary findings in a much more comprehensive and complete future amphibian habitat selection study of the area.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".