Bibliographic record
Abstract
The authors present an interesting study that has practical implications for wildfire management in Canada and potentially beyond.The authors explore the discrimination of grassland wildfires from agricultural/managed) fires in South Central Canada.Using terrestrial datasets and high-resolution Landsat 8 data, the authors carefully construct and classify a dataset of MODIS fire clusters and C1 NHESSD Interactive commentPrinter-friendly version Discussion paper explore the relationships between these two classes of fire and various environmental/meteorological variables using GAMs and regression tree (RT) models.The work results in a series of parameter thresholds and value ranges that appear to be useful for pinpointing periods when wildfires are most likely, and could likely be used to enhance operational wildfire management in future.This manuscript certainly merits publication in NHESS, however there are several areas where it could be improved prior to publication: The narrative and structure could be improved throughout (see specific comments) The methods need expanding, particularly with respect to the predictors chosen for inclusion in the models (some of this may be suited for inclusion in the supplementary materials).3) Some of the results/discussion points could be elaborated on further, and the importance of this work better highlighted.»> We thank the reviewers for their careful reading of the manuscript, and we document our responses and revisions below.Specific comments Abstract I would specifically refer to MODIS in the abstract, so it is immediately clear to readers what your primary RS dataset is.»>We revised the abstract to read: " Daily polar orbiting satellite MODIS thermal detections since 2002 were used as the baseline for quantifying wildfire. .."
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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 | 0.027 | 0.314 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.022 | 0.020 |
| Insufficient payload (model declined to judge) | 0.043 | 0.027 |
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".