Comparing four regression techniques to explore factors governing the number of forest fires in Southeast, China
Bibliographic record
Abstract
Four regression techniques, including two global models (i.e., Poisson and negative binominal) and two geographically weighted regression (GWR) models (i.e., geographically weighted Poisson regression (GWPR) and geographically weighted negative binominal regression (GWNBR)) were used to explore which was the most suitable method for predicting the number of forest fires and to investigate the spatially varying relationships between forest fires and environmental factors in Fujian province, in the Southeast of China. Our results showed that the GWR models fitted the fire count data better than the global models, and yielded more realistic spatial distributions of model predictions. Particularly, GWNBR was superior for addressing overdispersion in the fire count data because it estimated the dispersion parameter at a local level. Additionally, our study indicated that more forest fires occurred in areas of lower elevation, flatter terrain, and higher population density. The global models showed that precipitation had positive impacts on fire occurrence in the study area. In contrast, the GWR models revealed that precipitation was positively related to the forest fires in the western regions of Fujian, but negatively related in the eastern coastal regions. Our study could provide better insight into forest fire management based on local environmental characteristics.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".