Using landowner knowledge and field captures to determine habitat use by the northern prairie skink (Plestiodon septentrionalis) on exurban residential land in southwestern Manitoba
Bibliographic record
Abstract
Exurban development, consisting of low density residential housing in a rural setting, is steadily increasing in North America. This increase may have negative impacts on the habitat for some species, through the introduction of non-native plants and new predators such as house cats. The northern prairie skink (Plestiodon septentrionalis) is listed as Endangered in Canada occurring only in southwestern Manitoba. The objectives of this study included: a) defining prairie skink microhabitat use on private land according to vegetation, temperature and cover availability, b) determining landowner awareness of prairie skinks on their property, and c) determining how landowner stewardship could be used in skink conservation. Mixed methods strategy of inquiry was utilized and data collection procedures included both quantitative habitat surveys and qualitative landowner interviews. I found that prairie skinks were most often found in prairie habitat, and were found most often in areas with a) high percent artificial cover, b) high leaf litter, and c) more pieces of cover per acre. Landowners most often saw skinks near buildings, in flower beds and in debris piles. Landowner attitudes towards skinks were positive,though willingness may not translate into action.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".