Do roads affect the abundance of garter (<i>Thamnophis sirtalis</i>) and redbelly snakes (<i>Storeria occipitomaculata</i>)?
Bibliographic record
Abstract
The greatest driver of the current biodiversity crisis is habitat loss. Roads are a major contributor to habitat loss because they destroy and fragment habitat, in addition to causing direct mortality. Animals may respond to roads either by avoiding them, thus leading to population isolation, or by attempting to cross them, thus potentially leading to increased mortality and, if so, also to population isolation. We studied the impact of road density on abundance of two snake species: redbelly snakes ( Storeria occipitomaculata Storer, 1839) and garter snakes ( Thamnophis sirtalis Linnaeus, 1758) around Ottawa, Canada. We hypothesized that roads are detrimental to snake populations due to road avoidance and mortality. Therefore, we predicted that snakes should be less abundant at sites with higher road density in their surroundings. We deployed cover boards at 28 sites along a gradient of road density in 2020 and 2021. We visited sites weekly, counted the number of individuals of both species, and measured snout–vent length (SVL) of all individuals captured. We captured fewer garter snakes at sites surrounded by more roads and fewer redbelly snakes at sites surrounded by more urban habitat. Snakes at sites surrounded by more roads were not smaller. The effects of roads and urbanization on the number of snakes were modest, but indicate decreasing population sizes that could lead to loss of ecological function.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".