Bibliographic record
Abstract
The aims of this study are to (1) evaluate the factors influencing forest fire spread as fundamental data for forest fire spread modeling, and (2) predict the pattern of forest fire spread using GIS at a real time, understanding the interaction of factors. Chapter I addresses the statement of the background to this study and the methodology used. Chapter II summarizes previous researches on the factors influencing forest fire spread and constucts the databaserelated to it. Then, Chapter ill presents forest fire spread modeling using Arc/Info GRID and AML at a\n\nreal time and verifies it. Chapter N summarizes the conclusion of this study. The results of this study are as follows. (1) The pattern of spread is determined mostly by fuel and by topography and by the speed and direction of sUliace wind. (2) With GIS technology, map products can be created that not only analyze fire behavior but incorporates such information as the type of fuels, moisture content, and the topography effects associated with fire conditions. (3) Successful forest fire spread strategies rely on fire behavior models, which in turn rely on the accurate data of each environment. Mayor causes of forest fire are natural combustion in the United States but are man-caused fire in us. (4) In the United States and Canada, Forest fire spread modeling is applied to a gentle change of slope at local-scale topography. But, In Korea we need to do forest fire spread modeling considering a steep change of slope at it.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".