Data-based optimization of a simple shortwave fadeout absorption model
Bibliographic record
Abstract
Electron density enhancement caused by electromagnetic radiation emitted during a solar X-ray flare has the potential to increase high frequency (HF; 3–30 MHz) absorption in the dayside D-region ionosphere, impacting shortwave radio signals by reducing the signal strength, a phenomenon commonly referred to as shortwave fadeout. Data-based optimization of a simple absorption model is performed incorporating solar X-ray flux data and 30 MHz riometer data from stations distributed across Canada. In a single event study the data-based optimization model is shown to overestimate absorption by 1% for the duration of an X2.1 solar X-ray flare. This corrects an underestimation by the NOAA D-region Absorption Prediction (D-RAP) model. In a statistical study, based on 87 events, data-based optimization performed on an event-by-event basis showed excellent overall agreement between measured and modelled data: the Pearson correlation coefficient was R = 0.88, and the slope of the best-fit line to the data was m = 0.91. A generalized model was developed using data from all 87 events collectively. Although good agreement was found between the measured and modelled data sets, correlation and slope were slightly reduced to R = 0.75 and m = 0.80. Model accuracy is characterized by prediction efficiency (PE) which peaked at PE = 0.78 for the event-by-event evaluation and PE = 0.48 for the collective data set. The results of this study highlight the advantages of data-based optimization in modelling absorption due to shortwave fadeout.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".