The application of interactive multimedia CD-ROM technology to wildland fire safety training
Bibliographic record
Abstract
Interactive multimedia technology has been utilized in the development of a CD-ROM based wildland fire safety training course, Wildland Fire – Safety on the Fireline. Interactive multimedia technology allows delivery of training to a large number of students on a consistent basis. In addition, cost savings can be achieved through reduced learning time, reduced travel, minimal use of instructors, and most of all, through retention of knowledge as a result of using multimedia. The course, Wildland Fire – Safety on the Fireline, was developed and reviewed by a national team of specialists in wildland fire behavior and wildland fire safety with the intent of reducing and/or eliminating injuries and fatalities associated with the suppression of wildland fires. Wildland Fire – Safety on the Fireline focuses on due diligence, situational awareness, entrapment survival, health, equipment, and hazards encountered when working on the fireline. Each of the four sections comprising the course is followed by a board game test in preparation for a final test that is tracked by the computer. Key words: Canada, computer applications, fire behavior, fire entrapment avoidance, firefighter fatalities, firefighter physiology, fire suppression, fire survival, personal protective equipment, risk management, safe work practices, situational awareness, wildfire case studies, wildland firefighting, wildland-urban interface.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.040 | 0.004 |
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".