Hierarchy theory as a conceptual framework for scale issues in avalanche forecast modeling
Bibliographic record
Abstract
Abstract Although scale issues have been defined to be one of the most crucial topics in geosciences, they have received only limited attention in avalanche research. A thorough understanding of these issues in avalanche forecasting, however, is fundamental for the development of useful prediction models. After the definition of relevant terms, the different aspects of scale issues are introduced in detail. We present hierarchy theory (Ahl and Allen, 1996) as a potential framework for the multi-scale characteristics of the avalanche phenomenon. We suggest a temporal hierarchy, where the main contributing factors are ordered into seven levels according to their temporal-scale characteristics with respect to avalanche forecasting. Within each level there are individual spatial hierarchies, which result in a two-dimensional structure. This process-oriented framework is compared to the data classification scheme of La Chapelle (1980), which is based on informational entropy. Scale issues in prediction modeling and the consequences of the new framework for modeling efforts are discussed in detail.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".