Bibliographic record
Abstract
The predominant campestral vegetation in the state of Rio Grande do Sul, as well as all types of vegetal formations, may be considered as a dynamic system subject to several disturbance agents. Fire is frequently mentioned as one of them, and may present natural or anthropic causes. Occasional burns are mainly a result of electric discharges from the atmosphere. Intentional burns may be controlled or not and are usually associated to the management of areas aimed at agriculture and cattle raising activities. Thus, the objective of this research was to perform a study on accidental field burns in order to characterize and to identify places of higher burn incidence in the county of Santa Maria RS, Brazil, and to aid in the planning and control of fires, correlating the number of burns with meteorological elements in order to identify the most propitious conditions for the occurrence of these events. The interest variable (response) in this study was the number of daily calls received by the Santa Maria Fire Brigade obtained from its records within the period from January 1st 1993 to December 31st 2004. This variable was explained by meteorological elements such as: maximum and minimum temperature; relative air humidity measured at 9:00am, 3:00pm and 9:00pm; insolation; rain precipitation and average wind velocity at the day of occurrence and by the number of days without any pluviometric precipitation before the occurrence of the interest variable. It was verified that the Fire Brigade received 1.81 daily calls, on average; that the call was preceded by a dry period of four days on average, and that most burns occurred in the afternoon and at the almost uninhabited RS 287 highway alongside region. The month in which the Fire Brigade received the highest number of calls was August, and the year of 1999 was the one presenting the highest occurrence of field burns. Moreover, the number of calls was equally distributed along the weekdays. Based on quartiles, city districts with high, intermediate and low chances for the occurrence of burns were determined, and regions alongside the highway and the following city districts: Distrito Industrial, Medianeira, Itararé, Tomazzetti and Parque Pinheiro Machado were those presenting the highest chances for the occurrence of burns. Based on the correlation between dependent variable and all independent variables, it was verified that the variable with the highest correlation with the number of calls received by the Fire Brigade was the relative air humidity. The evaluation of the twenty-four models tested revealed that in all of them, the presuppositions were violated, being therefore, inappropriate for the forecast of the independent variable.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".