Comparison of spatial vegetation patterns following clearcuts and fires in Ontario's boreal forests
Bibliographic record
Abstract
The goal of this study was to compare spatial vegetation patterns, based on Landsat TM \ndata, within post-clearcut and post-fire disturbances. Landscapes disturbed during the \nfour decades prior to the collection date of the Landsat data were used for comparison. \nThe disturbed landscapes were clustered according to their spatial edaphic factor \npatterns. A suite of indices representing patch geometry, contagion, and composition \nwere used to describe spatial vegetation and edaphic factor patterns. A general linear \nmodel was used to compare the effects of disturbance type, time since disturbance, and \nedaphic factors (clusters) on seven indices of spatial vegetation patterns. \nPatch size and patch density differed following clearcuts and fires. It appears that \nclearcuts may result in greater spatial heterogeneity among landcover types compared to \nfires. I propose that fires were more severe than clearcuts; thus, creating larger and \nfewer patches. Time since disturbance had the greatest effect on spatial vegetation \npatterns. One decade old disturbances had larger patches, higher contagion and fewer \nlandcover types than older disturbances. I suggest that spatial vegetation patterns \nreflected the destruction of overstory vegetation in one decade old disturbances, and \nrevegetation in the form of small patches in older disturbances. It appears that the effects \nof disturbance on spatial vegetation patterns are temporary. Edaphic factor patch shapes \nmay influence the shape of vegetation patches.
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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 | 0.000 | 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".