Multifractal Detrended Fluctuation Analysis, Cross-Correlation and Clustering of Global 7Be Activity Concentration
Bibliographic record
Abstract
Daily [Formula: see text] activity concentrations data across 21 global locations between 2010 and 2017 from the Comprehensive Nuclear-Test-Ban Treaty Organization (CTBTO) were analyzed using multifractal formalism. The multifractal detrended fluctuation analysis revealed that [Formula: see text] distribution across the 21 locations are multifractal ([Formula: see text]) with a wide range of fractal exponents. The observed multifractality was found to be statistically significant except at two locations (RN45 and RN47). The multifractal strength ([Formula: see text]) and Holder’s exponent ([Formula: see text]) were used to group the locations into 3 clusters with K-means algorithm. The relationship between [Formula: see text] and five drivers (Southern Oscillation Index — SOI, North Atlantic Oscillation — NAO, Total Sunspot number — Tot_SN, Northern hemisphere Sunspot number — NH_SN and Southern hemisphere sunspot number — SH_SN) was investigated using multifractal detrended cross-correlation analysis. The multifractal cross-correlation between [Formula: see text] and drivers was found to be 0.06–0.21(SOI), 0.08–0.23 (NAO), 0.04–0.27 (Tot_SN), 0.05–0.25 (NH_SN) and 0.04–0.27 (SH_SN). NAO was found to be the strongest driver of [Formula: see text]. The location, RN16 in Yellowknife Canada, showed strong cross-correlation with the five drivers.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".