A GIS-based lightning-caused fire probability prediction model in Daxing'an Mountains of Heilongjiang Province
Bibliographic record
Abstract
This paper presents a gis-based model to predict the probability that a lightning strike will cause a sustainable ignition in Daxing'an Mountains in Heilongjiang Province. Daily data of lightning position, climate factors, combustible and the number of fires caused by lightening from April 2006 to October 2011 in the studied area were collected to develop a fitting model by logistic regression. In the process, Canadian forest fire weather index(FWI) was introduced to select independent variables and topographic features together with characteristics of lightning-caused fire were considered when independent variables were calculated. A good fitting result was showed by Hosmer-Lemeshow test. The correlation between daily observed and modeled number of lightning-caused fires is not significantly different from y=x. The developed forest lightening fire predicting system based on the model was put into a trial operation in Daxing'an Mountains successfully predicted 31.5% of all the fires.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".