The Classification and Characterization of Canadian Boreal Peatland Sub-classes
Bibliographic record
Abstract
This research addresses the lack of suitable peatland maps and vegetation data in the Canadian Boreal Forest.This study aimed to create a peatland sub-class map and inventory peatland vegetation height.A three-stage hierarchical classification framework was developed for mapping peatland sub-classes circa 2020.A combination of multi-spectral data, L-band SAR backscatter and C-Band InSAR coherence, forest structure, and ancillary variables were used as model predictors.In the first stage, wetlands, uplands, and water were classified with 86.5% accuracy.The second stage achieved 93.3% accuracy in distinguishing peatland from mineral wetlands.The third stage, focusing on peatland areas, classified bogs, rich fens, poor fens, and permafrost peat complexes with 71.5% accuracy.ICESat-2 ATL08 data described regional and class-wise vegetation height variations.This research introduced a comprehensive large-scale peatland sub-class mapping framework for the Canadian Boreal Forest, presenting the first moderate resolution map of its kind.i I would like to acknowledge the many people who made the writing of this thesis possible.First, I would like to thank my supervisor Dr. Koreen Millard for her invaluable advice, constant guidance, and continuous support.I would also like to acknowledge my committee members, Dr. Dan K. Thompson and Luc Guindon for their continuous guidance and contributions of expertise
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 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".