Visitor Risk Management Applied to Avalanches in New Zealand
Bibliographic record
Abstract
The Department of Conservation (DOC) manages a third of the land in New Zealand. This is also where the majority of avalanches occur. Many huts, tracks and popular recreational areas are in avalanche terrain. In 2009 DOC finalised a visitor risk management policy that included six underlying principles for managing risk to visitors. In summary they are: The Department will; preserve the range of recreation experiences, facilities are safely situated in accordance with the predominant visitor group and the Department will provide appropriate information about hazards. Visitors will; be responsible for the decision they make and be responsible for their own skills and competence. Concessionaires (tour operators) will be responsible for the safety of their clients. DOC applies a range of avalanche risk mitigations to the six visitor groups it uses to classify visitors to land managed by the Department. This forms a continuum with a high level of care for the accessible front country through to minimal input for the remotest part of backcountry. This paper will show how avalanche risk management is integrated with DOC’s visitor risk management policy and system to manage visitor risk. Avalanche risk mitigations include avalanche path mapping of huts. The introduction of the Canadian Avalanche Terrain Exposure Scale (ATES) for the backcountry and the application of site management using an avalanche hazard index for high use places where there are users with limited avalanche knowledge. In partnership with the Mountain Safety Council DOC assists with a number of avalanche hazard advisories.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".