High-risk area monitoring and early warning model for mountain tourism based on hyper-spectral
Bibliographic record
Abstract
The carrier of mountain tourism is the natural environment, and the high-risk areas in the natural environment are the main hidden dangers causing safety problems, while the existing monitoring technology has the problems of low resolution, low accuracy and high false alarm rate. In this paper, a high-risk area monitoring and early warning model for mountain tourism based on hyper-spectral image(HSI) is proposed, which is called HMW, it collects Hyper-spectral images of high-risk areas through a hyper-spectral equipment and analyzes the details of high-spectral images on the Hadoop big data platform, then compose a comprehensive threshold function CEW() from multiple indicators, and trigger an alarm when the value of CEW()is greater than the risk threshold. For different types of high-risk areas, the coefficients in the CEW() function can also be adjusted, so that the CEW() function has versatility practicality. It can be seen from the simulation experiment results of the HMW model that the HMW model has the advantages of high accuracy, timely feedback and low false-positive rate, with a delay of less than 26 seconds, and can accurately and timely feedback the safety status of high-risk areas of mountain tourism.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".