On the predictive potential of Pc5 ULF waves to forecast relativistic electrons based on their relationships over two solar cycles
Bibliographic record
Abstract
Abstract A statistical study of relativistic electron (>2 MeV) fluence derived from geosynchronous satellites and Pc5 ultralow frequency (ULF) wave power computed from a ground magnetic observatory data located in Canada's auroral zone has been carried out. The ground observations were made near the foot points of field lines passing through the GOESs from 1987 to 2009 (cycles 22 and 23). We determine statistical relationships between the two quantities for different phases of a solar cycle and validate these relationships in two different cycles. There is a positive linear relationship between log fluence and log Pc5 power for all solar phases; however, the power law indices vary for different phases of the cycle. High index values existed during the descending phase. The Pearson's cross correlation between electron fluence and Pc5 power indicates fluence enhancement 2–3 days after strong Pc5 wave activity for all solar phases. The lag between the two quantities is shorter for extremely high fluence (due to high Pc5 power), which tends to occur during the declining phases of both cycles. Most occurrences of extremely low fluence were observed during the extended solar minimum of cycle 23. The precursory attribute of Pc5 power with respect to fluence and the enhancement of fluence due to rising Pc5 power both support the notion of an electron acceleration mechanism by Pc5 ULF waves. This precursor behavior establishes the potential of using Pc5 power to predict relativistic electron fluence.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".