Anthropogenic factors in a forest and peatland fire danger rating system in South Sumatra
Bibliographic record
Abstract
Abstract In an effort to prevent forest and land fires, Indonesia has implemented its fire danger rating system (FDRS). Currently, the FDRS is based upon the Canadian system, which is driven by only weather parameters. However, fires in Indonesia are primarily caused by human activities with most of them occurring on peatlands. Thus, it is necessary to integrate these factors in the development of the next generation FDRS to increase the precision and accuracy in assessing levels of fire danger in various Indonesian regions. This study presents a proof of concept for a new FDRS, applied in forest and peatland in Ogan Komering Ilir (OKI) District, South Sumatra Province, based on three parameters: weather, anthropogenic factors and presence or absence of peat. The anthropogenic and peatland FDRS (AP-FDRS) map was produced using a spatial logistic regression method for incorporating anthropogenic and peat models, using the fire weather index (FWI) model from forest and land fire early warning system together with an overlay method to produce a composite map of forest and peatland fire. In this study, 2 d in 2020 were selected as sample dates: one with hotspot occurrences (23 August 2020) and one without hotspots (28 December 2020). On 23 August 2020, the AP-FDRS map was dominated by high (54%) and extreme (46%) fire hazard levels after combining high FWI values with extreme AP risks in the OKI District. In contrast, on 28 December 2020, although the FWI indicated low fire risk, the integration with AP factors resulted in an AP-FDRS map dominated by medium (34%) and high (60%) fire hazard levels. These results highlight that weather factors alone are not sufficient to predict fire hazard risks in Indonesia. Therefore, the AP-FDRS provides more detailed and accurate predictions compared to current FWI based systems.
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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 | 0.001 |
| 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".