Solar activity variability in the IRI at high latitudes: Comparisons with GPS total electron content
Bibliographic record
Abstract
Abstract Total electron content (TEC) measurements from 10 dual‐frequency GPS receivers in the Canadian High Arctic Ionospheric Network (CHAIN) are used to evaluate the performance of International Reference Ionosphere (IRI)‐2007 within the Canadian sector. Throughout the region, we see systematic underestimation of daytime TEC, particularly at solar maximum, where summer and equinox root‐mean‐square errors reach as high as 14 total electron content units, 1 TECU = 1016 el m−2 (TECU). It is also shown that the use of a monthly IG index, in place of the IRI's standard IG12 index, leads to an improvement in TEC specification by up to 3 TECU in the polar cap and up to 6 TECU in the subauroral region during periods of short‐term, large amplitude changes in solar activity. On diurnal timescales, variability in TEC is found to be underestimated by the IRI, during equinox periods, by up to 40% at subauroral latitudes and up to 70% in the polar cap region. During the winter, diurnal variations are overestimated by up to 40% in the subauroral region and are underestimated within the polar cap by up to 80%. Using collocated ionosonde data, we find IRI bottomside TEC to be within 1 TECU of observation with errors largest during the equinoxes. For the topside we find good agreement during the winter but significant underestimation of topside TEC by the IRI during summer and equinox periods, exceeding 6 TECU at times. By ingesting measured NmF2 into the IRI, we show that the topside thickness parameterization is the source of the bulk of the observed TEC errors.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".