Bibliographic record
Abstract
One of the requirements for a self-organized critical system is scale invariance in the time or frequency domain. Sandpile avalanches and other types of avalanches have been analyzed from the perspective of common characteristics of critical systems. However, snow avalanches have not been completely analyzed, particularly in the frequency domain. Snow avalanches constitute a natural hazard and they are of much more practical importance than other types of avalanches so far analyzed. In this paper, a waiting time analysis of 15,990 slab avalanche events collected in two avalanche areas over 23 years from 110 avalanche paths is given. The objective is to analyze the frequency spectrum for waiting time between avalanches for scale invariance and the presence of 1/f noise as suggested for a characteristic of self-organized criticality. The assumptions of the rare events approximation are used: namely that the events and paths are independent, they do not overlap in time and space and the probability of events is small over a suitable time scale. Random Poisson events are assumed with the waiting time being exponential for an individual avalanche path. The results of the analyses show that a 1/f frequency spectrum is unlikely for either avalanche area over any significant range of frequency. Further, scale invariance of waiting time is possible for the entire avalanche areas only over a short time frame of a few hours. However, self-similar clustering of event waiting times is possible for some individual avalanche paths with long mean waiting times between events. Mean waiting times range between about 2 days to 540 days for the individual paths. In combination, the results suggest that neither the time arrival nor waiting time between avalanche events conform to that of a critical system as defined for self-organized criticality or thermodynamics. If snow avalanches are to conform to a critical system in geophysics then a revision of the requirements or definition is called for.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".