Toward peatland fire vulnerability monitoring using surface water maps: A geospatial and temporal analysis of peatland water bodies using Synthetic Aperture RADAR
Bibliographic record
Abstract
Peatlands, essential ecosystems with unique hydrological characteristics, are increasingly vulnerable to fire events due to climate change.The primary objective of this thesis is to test fire vulnerability monitoring in peatlands through the mapping of peatland water bodies, aiming to assess the hydrologic conditions over time.Employing geospatial analysis techniques, the study investigates the backscatter attributes and spatial patterns of these water bodies, elucidating their role in peatland hydrology and their impact on fire dynamics.This information is applied to test the design of Random Forest surface water classification models which test model parameters and training data schemes to determine the best surface water classification model.The characterization of small peatland water bodies and the testing of these models offers a strong foundation for a surface water classification that can capture surface water in peatlands, provided more intentional training data which represents the various conditions in peatlands is added.
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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 | 0.000 | 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 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".