BENEFIT-DETRIMENT ANALYSIS FOR NEW RESCUE TECHNOLOGIES
Bibliographic record
Abstract
ABSTRACT: Organizations involved with public avalanche safety, such as the Canadian Avalanche Centre (CAC) are looked to for advice about whether to adopt new avalanche rescue technologies, such as avalanche search apps for smartphones. However, deciding on the best advice to give is not always a straightforward task. This paper outlines a method of using a formal benefit-detriment analysis for evalu-ating the overall impact of a new rescue technology. The method is fairly simple and first requires identify-ing both positive and negative aspects of the new technology, including impacts on existing rescue systems. A discussion on quantifying some of the benefit and detriment components is included. Applying this method to the case of available smartphone avalanche search apps, we argue that in their current form they are most likely detrimental to public avalanche safety, regardless of their uptake level. There are potentially different methods for utilizing smartphone technology in avalanche rescue scenarios. Us-ing the same analysis technique, we explore scenarios where smartphone technology could potentially have a favorable impact on avalanche safety. This technique has applicability for organizations trying to decide whether or not to recommend or adopt new or even existing avalanche rescue technologies.
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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 | 0.012 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".