Fire Monitoring Method of Ancient Building Repair Stage Based on Machine Learning Algorithm
Bibliographic record
Abstract
Fires in old structures have increased due to over-exploitation of tourists and the use of electrical equipment, inflicting substantial social and economic losses. With computers, statistical learning and optimization theory preceded machine learning. Numerous algorithms for various disciplines and issues have been proposed. Tibet has a plateau climate. A building's fire risk includes both property damage and personnel and property loss. The building's vulnerability depends on their joint deterioration. When the global positioning system falls short, machine learning-based outside mobile terminal placement compensates. No hardware is needed for machine learning-based outside mobile terminal location. This document evaluates fire risk based on building status, fire source control, fire control facilities, personnel evacuation facilities, and fire control safety management. This study summarizes and analyzes ancient building rehabilitation technology after fire and explores viability schemes under diverse circumstances to help develop it. In this work, the needle-robot learning algorithm can be adapted to fire during the repair stage of most historic buildings, but there are various types of ancient buildings, thus the evaluation index of different types of ancient buildings must be refined in the future.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".