Testing the Microclimatic Habitat Design Framework in Abandoned Sand and Gravel Extraction Sites Using the Karner Blue Butterfly
Bibliographic record
Abstract
Microclimatic planning is an important component in rehabilitating landscapes. Rehabilitation practices often simplify a landscape, reducing microclimatic complexity and decreasing the capacity of the land to support a variety of niche habitats. This study took a target-species approach to rehabilitation, applying the specific microclimatic requirements of the Karner blue butterfly (<i>Lycaeides melissa samuelis</i>) using a microclimatic habitat design framework. Incident solar radiation was modeled for a range of slopes and aspects, and wind was modeled from different directions of flow. These results were applied to three aggregate (sand and gravel) extraction sites in Ontario, Canada. Microclimate units were mapped through a geographic information system, and the results were evaluated in terms of their capability for meeting the microclimatic habitat needs of the Karner blue butterfly. The framework provides a comprehensive rehabilitation planning framework that may be applied to a variety of focal species and climate regions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".