Air and surface temperature modelling across a temperate mountain landscape: An investigation of microclimatic influences on surface offsets viewed within the context of epigaeic arthropod thermal habitat
Bibliographic record
Abstract
Abstract Development of high‐resolution temperature models in mountain environments must include consideration of the influence of complex topography and seasonality on thermal distribution across horizontal and vertical scales. Small‐bodied organisms, including arthropods, in montane and alpine ecosystems inhabit environments for which local microclimate and heat transfer is especially important. We developed and applied high‐resolution air and surface temperature models for a remote mountain environment using in‐situ data for interpolation procedures in ArcGIS Pro. This approach requires recording directional and time‐period specific lapse rates to aid in the development of air temperature models. Also examined is the offset between air temperature and surface temperature and to what extent air temperature alone is a reliable indicator of ground‐level thermal conditions. We describe an environmentally inclusive surface temperature modelling method that allows for the addition of explanatory layers (landcover, elevation, aspect, slope, and topographic position index) aiding in the interpolation process. These models are used to delineate thermally defined ecological zones and model unique thermal properties of relevance to arthropods across the southern Alberta study area.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".