Overloaded! Critical revision and a new conceptual approach for snow indicators in ski tourism
Bibliographic record
Abstract
Indicators are widely used in climate variability and climate change assessments to simplify the tracking of complex processes and phenomena in the state of the environment. Apart from the climatic criteria, the snow indicators in ski tourism have been increasingly extended with elements that relate to the technical, operational, and commercial aspects of ski tourism. These non-natural influencing factors have gained in importance in comparison with the natural environmental conditions but are more difficult to comprehend in time and space, resulting in limited explanatory power of the related indicators when applied for larger/longer scale assessments. We review the existing indicator approaches to derive quantitative measures for the snow conditions in ski areas, to formulate the criteria that the indicators should fulfill, and to provide a list of indicators with their technical specifications which can be used in snow condition assessments for ski tourism. For the use of these indicators, a three-step procedure consisting of definition, application, and interpretation is suggested. We also provide recommendations for the design of indicator-based assessments of climate change effects on ski tourism. Thereby, we highlight the importance of extensive stakeholder involvement to allow for real-world relevance of the achieved results.
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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 | 0.050 | 0.092 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.008 | 0.014 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".