Segmental interpolating spectra for solar particle events and in situ validation
Bibliographic record
Abstract
It is a delicate task to accurately assess the impact of solar particle events (SPEs) on future long-duration human exploration missions. In the past, researchers have used several functional forms to fit satellite data for radiation exposure estimation. In this work we present a segmental power law interpolating algorithm to stream satellite data and get time series of proton spectra, which can be used to derive dosimetric quantities for any short period during which a single SPE or multiple SPEs occur. Directly using the corrected High Energy Proton and Alpha Detector fluxes of GOES, this method interpolates the intensity spectrum of a typical SPE to hundreds of MeV and extrapolates to the GeV level as long as sufficient particles are recorded in the high-energy sensors. The high-energy branch of the May 2012 SPE is consistent with the Band functional fitting, which is calibrated with ground level measurement. Modeling simulations indicate that the input spectrum of an SPE beyond 100 MeV is the major contributor for dose estimation behind the normal shielding thickness of spacecraft. Applying this method to the three SPEs that occurred in 2012 generates results consistent with two sets of in situ measurements, demonstrating that this approach could be a way to perform real-time dose estimation. This work also indicates that the galactic cosmic ray dose rate is important for accurately modeling the temporal profile of radiation exposure during an SPE.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".