Statistical Study of the Occurrence of Coronal Holes in the Solar Corona During Solar Cycle 24
Bibliographic record
Abstract
In this article we look at: (a) the occurrence of coronal holes as a function of the different phases of solar cycle 24, (b) the variability of the annual number of coronal holes from 2009 to 2019, (c) the distribution of polar and non-polar coronal holes, irrespective of the phase but also as a function of the different phases of the cycle. From the phase minimum to the descending phase of solar cycle 24, the occurrences of coronal holes observed on the solar disc are 38%, 24%, 9% and 29% respectively. The annual number of coronal holes observed on the solar disc does not always follow the expected evolution of the two components of the solar magnetic field and the annual number of sunspots. Regardless of the phase of the solar cycle, the majority of coronal holes, around 65%, are located in the polar regions. During the early and late phases of the solar cycle, polar coronal holes predominate, whereas during the ascending phase and phase maximum, non-polar coronal holes predominate. The number of polar coronal holes is highest during the minimum phase of the solar cycle and lowest during the maximum phase. During the phases of the solar cycle, coronal hole activity appears to be in phase opposition with sunspot activity. There is a fairly good correlation between variations in the number of polar coronal holes and the expected evolution of the poloidal component of the solar magnetic field.
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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 | 0.005 |
| 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 | 0.001 | 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".