A model of fragmentation in the Canadian boreal forest
Bibliographic record
Abstract
Ecological studies have generally examined forest fragmentation in terms of descriptive metrics or simulation using Monte Carlo or percolation processes that assume fragmentation is a random process. However, most fragmentation results from human decisions on agricultural settlement. This study used a previously tested rule-based agricultural settlement process model (GEOMOD2) to describe which parts of a boreal forest landscape are selectively cleared for agriculture. Nearness to neighbors, amount of stoniness, soil type, and soil texture best explained the fragmentation process. To compare settler's decisions on the productivity of the landscape with moisturenutrient gradients, we used a hydrological topographic index to capture the variability of wetness according to hillslope position. Results showed that settlers were selecting higher hillslope positions irrespective of substrate (glaciolacustrine or glacial till); i.e., they appear to have used observable attributes such as stoniness, soil texture, and hillslope position rather than soil productivity in making settlement decisions. Thus, the species richer upper hillslopes of aspen parkland (glaciolacustrine) and aspen and white spruce forest (glacial till) were settled first, while the species poorer lower hillslopes of aspen forest (glaciolacustrine) and white spruce and balsam fir forest (glacial till) were settled later.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".