Analysis of Solar Activity and Atmospheric Pressure Competition Effects on Cosmic Radiation Events
Post-publication record
Source: Retraction Watch, joined by DOI. OpenAlex records retraction as is_retracted, a boolean over a state space with at least four values, so it cannot express an expression of concern, a correction or a reinstatement; it reports them as false, which reads as “fine”.
Bibliographic record
Abstract
In this paper, we analyze the influence of characteristic solar activity parameters as well as the competition effects in solar activity and atmospheric pressure on the records of cosmic radiation based on HiSPARC (The High School Project on Astro physics Research with Cosmic rays), a ground-based muon detector system monitoring secondary cosmic ray intensity.We gather the data from No.501 HiSPARC station situated at Nikhef in Science Park, Amsterdam, Netherlands (52.3558963°N, 4.9509827°E, 56.18 m of altitude).The accepted anticorrelation between solar activity and the intensity of near ground cosmic ray is confirmed by comparing the number of solar flares, relative number of sunspots and counting rates of the detector.The barometric effect has been considered to correct the number of events.Furthermore, a new empirical equation is given to show the correlation between the cosmic ray intensity and the characteristic solar activity parameters, including the number, the area and the latitude of sunspot groups.As for the competition effects of solar activity and atmospheric pressure, we find there exists a threshold in detector's sensitivity to the sunspot numbers.Above the threshold, the detector is more sensitive to the atmospheric pressure rather than the solar activity.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".