Modeling the spatial distribution of subarctic forest in northern Manitoba using GIS-based terrain and climate data
Bibliographic record
Abstract
Northern forested ecosystems are predicted to change dramatically in response to climate change during the next century. The purpose of this paper was to use logistic regression analysis to model the effects of climate and topography on the spatial distribution of the northern forest around Churchill, Manitoba, Canada. Climate maps were modeled using kriging interpolation of actual climate data collected from 34 long-term monitoring sites distributed throughout the study area, and topographic information was derived from commercially available digital elevation models. Five of the 18 independent variables contributed appreciably (p < 0.15) to the final logistic regression model: distance from the Hudson Bay coast, summer soil temperature, snow density, slope, and snow water equivalent. Current forest distribution was predicted with 66% accuracy using the final model, and Kappa statistics indicated significant agreement between modeled and actual forest extents. Significant explanatory variables demonstrate important synergistic effects of Hudson Bay, wind, and snow in determining forest distribution. Modeled forest extents were further south than actual forest limits, which suggest that the treeline is not likely in equilibrium with the present climate.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".