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 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 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.001 | 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".