Research Article Drainage ditches facilitate frog movements in a hostile landscape
Bibliographic record
Abstract
Ditches are common in landscapes influenced by agricultural, forestry, and peat mining activities, and their value as corridors remains unassessed. Pond-breeding amphibians can encounter hostile environments when moving between breeding, summering, or hibernation sites, and are likely to benefit from the presence of ditches in the landscape. Within a system consisting of ditch networks in bogs mined for peat in eastern New Brunswick, Canada, I quantified the breeding, survival, and movements of green frogs (Rana clam-itans melanota) in drainage ditches and also surveyed peat fields. Frogs rarely ventured on peat fields and most individuals frequented drainage ditches containing water, particularly in late summer. Though frogs did not breed in ditches, their survival rate in ditches was high (88%). Ditches did not hinder frog movements, as frogs moved independently of the current. Results indicate that drainage ditches containing water enable some movements between habitats isolated by peat mining, in contrast to peat surfaces, and suggest they function as amphibian movement corridors. Thus, such drainage ditches may mitigate the effects of peat extraction on amphibian populations. At the very least, these structures provide an alter-native to hostile peat surfaces. This study highlights that small-scale corridors are potentially valuable in population dynamics.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".