Bibliographic record
Abstract
Solar Cycle 24 has almost faded and the activity of Solar Cycle 25 is appearing. We have learned much about predicting solar activity in Solar Cycle 24, especially with the data provided by SDO and STEREO. Many advances have come in the short-term predictions of solar flares and coronal mass ejections, which have benefited from applying machine learning techniques to the new data. The arrival times of coronal mass ejections is a mid-range prediction whose accuracy has been improving, mostly due to a steady flow of data from SoHO, STEREO, and SDO. Longer term (greater than a year) predictions of solar activity have benefited from helioseismic studies of the plasma flows in the Sun. While these studies have complicated the dynamo models by introducing more complex internal flow patterns, the models should become more robust with the added information. But predictions made long before a sunspot cycle begins still rely on precursors. The success of some categories of the predictions of Solar Cycle 24 will be examined. The predictions in successful categories should be emphasized in future work. The SODA polar field precursor method, which has accurately predicted the last three cycles, is shown for Solar Cycle 25. Shape functions for the sunspot number and F10.7 are presented. What type of data is needed to better understand the polar regions of the Sun, the source of the most accurate precursor of long-term solar activity, will be discussed.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".