Winter snowfall determines the occupancy of northern prairie wetlands by tadpoles of the Wood Frog (<i>Lithobates sylvaticus</i>)
Bibliographic record
Abstract
In the northern plains of North America, the wetland breeding habitat of amphibians and their populations could be reduced by a change in climate that included decreased precipitation. To test this hypothesis, relative abundance of late-stage tadpoles of the Wood Frog ( Lithobates sylvaticus (LeConte, 1825)) was monitored from 1997 to 2010 during a wet–dry–wet cycle in 29 wetlands distributed throughout central Saskatchewan, Canada. The wetlands were dry for up to 7 consecutive years, and for a mean of 3.8 consecutive years. Consequently, tadpole occupancy of the wetlands was reduced to less than 40% for 5 consecutive years and none of the wetlands had tadpoles during the severe drought of 2001 and 2002. However, the drought had no observable long-term effect on either tadpole occupancy of wetlands or tadpole abundance. In 2007, 93% of the wetlands supported tadpoles, and in 2008 the highest mean relative abundance of tadpoles was recorded. Tadpole occupancy of wetlands was related to winter and spring precipitation (R2 = 0.84) with 67% of long-term variation in occupancy related to snowfall from November to February and 17% related to rainfall from March to June. Less than 45 mm of winter precipitation for 6 consecutive years would probably cause regional extinction of populations of the Wood Frog.
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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".