Modelo de alerta de intensidade de risco de fogo utilizando algoritmo de regressão logística ordinal de classificação
Bibliographic record
Abstract
The goal of the study is to analyze the Ordinal Logistic Regression model to predict the classification classes associated with the risk of fire attribute. For the study provided, it was considered the data of the dataset related to the Programa Queimadas of Instituto Nacional de Pesquisas Espaciais(INPE), and also the data from the Instituto Nacional de Meteorologia (INMET), both of them with the data for the Parque acional do Araguaia(TO), Brazil. The idea was to validate a classification model capable of creating meaningful insights for the community, or reserve parks, making it easier to get the information associated with the risk of fire. The results of the study resulted in an accuracy of 82.13%, by comparing the classification results with the dataset associated with the test. An important consideration to be done for future studies is that the dataset analyzed was unbalanced, impacting considerably the results presented.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".