Effect of variable retention forestry on wood frogs (<i>Lithobates sylvaticus</i>) in early successional boreal mixedwood forests
Bibliographic record
Abstract
Variable retention forest harvesting aims to reduce negative effects of harvesting on forest biodiversity, but knowledge gaps remain regarding its effects on some taxa over longer post-harvest time frames. To better understand effects of variable retention and environmental features on amphibians, we used pitfall traps to capture wood frogs ( Lithobates sylvaticus (LeConte, 1825)) across four levels of retention (clearcut (0%), 20%, 50%, and unharvested control (100%)), and two forest types (deciduous and coniferous), in 17-year post-harvest forests in northwest Alberta. We mapped breeding sites and used a LiDAR-based terrain moisture index (depth-to-water) to examine relationships between relative abundance, breeding site proximity, and soil moisture. Retention level alone had no effect on relative abundance of adult wood frogs, but in late summer (July and August), there was a significant interaction between retention level and forest type: capture rates decreased with retention level for deciduous forests, but increased with retention level in conifer forests. During late summer, capture rates were higher in conifer forests than deciduous forests, with soil moisture (lower depth-to-water) positively related to capture rates. Though timber retention may be beneficial to wood frogs in the short term, any impacts of forest harvesting on wood frog abundance were undetectable in stands 17 years post-harvest.
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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 | 0.001 | 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".