Utility of Classification and Regression Tree Analyses and Vegetation in Mountain Permafrost Models, Yukon, Canada
Bibliographic record
Abstract
ABSTRACT Classification and regression tree (CART) analyses were undertaken to test the usefulness of including vegetation variables in mountain permafrost distribution models for five widely spaced study areas in the Yukon. Digital elevation model (DEM)‐derived variables, field‐derived vegetation variables and satellite imagery‐derived vegetation variables were employed individually to classify sites into permafrost probable, permafrost improbable and permafrost ‘uncertain’ categories. The vegetation variables were subsequently combined with the DEM‐derived set to see if they could improve the latter's accuracy. Overall training accuracies for the probable and improbable permafrost categories for 102 sites ranged from 81% to 92%. Remotely sensed imagery alone had the lowest overall training (81%) and testing (50%) accuracies. The CART that combined imagery and DEM‐based variables produced high overall accuracy for training (90%) and the highest for testing (77%), had few nodes classified as ‘uncertain’ and could be used to create permafrost probability maps of the study areas. CART analyses appear useful for predicting permafrost distribution because they can incorporate non‐linear relationships between independent variables and the presence of permafrost. Remotely sensed variables relating to vegetation, specifically a normalised difference vegetation index, improved the DEM‐based results, but required considerable additional effort for data collection and processing. Copyright © 2011 John Wiley & Sons, Ltd.
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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 | 0.000 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".