Bibliographic record
Abstract
One of three criteria to demonstrate self‐organized criticality (SOC) for a critical phenomenon is that time arrival of events displays a frequency dependence which is inversely proportional to frequency (f) to some power. That is, for SOC, the power spectrum in the frequency (f) domain is supposed to fall off as 1/fβ, where β is typically a number between 1 and 2. Avalanche phenomena have been used as prototypes for illustrating SOC, and therefore it is of interest as to whether snow avalanches follow the criterion. In this paper, time series analyses of mass arrivals from 20 years of records constituting ∼10,000 avalanches are presented for Bear Pass and Kootenay Pass, British Columbia. The results suggest that the autocorrelation functions and partial autocorrelation functions of the series fall off in an exponential manner so that the implied power spectra in the frequency domain, given by the Fourier transforms of the autocorrelation functions, decay with frequency in a manner which is not strictly consistent with SOC. In common with SOC, the power spectra are suggested to have most content in the low‐frequency events and the spectra do not constitute white noise. However, given the limitations on the data sampling and recording, it cannot be definitively stated that the power spectra fall off with 1/f® as required for SOC.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".