E‐CHAIM as a Model of Total Electron Content: Performance and Diagnostics
Bibliographic record
Abstract
Abstract Here, we assess to what extent the Empirical Canadian High Arctic Ionospheric Model (E‐CHAIM) can reproduce the climatological variations of vertical Total Electron Content (vTEC) in the Canadian sector. Within the auroral oval and polar cap, E‐CHAIM is found to exhibit Root Mean Square (RMS) errors in vTEC as low 0.4 TECU during solar minimum summer but as high as 5.0 TECU during solar maximum equinox conditions. These errors represent an improvement of up to 8.5 TECU over the errors of the International Reference Ionosphere (IRI) in the same region. At sub‐auroral latitudes, E‐CHAIM RMS errors range between 1.0 and 7.4 TECU, with greatest errors during the equinoxes at high solar activity. This represents an up to 0.5 TECU improvement over the IRI during summer but worse performance by up to 2.4 TECU during the winter. Comparisons of E‐CHAIM performance against in situ measurements from the European Space Agency's Swarm mission are also conducted, ultimately finding behavior consistent with that of vTEC. In contrast to the vTEC results, however, E‐CHAIM and the IRI exhibit comparable performance at Swarm altitudes, except within the polar cap, where the IRI exhibits systematic underestimation of electron density by up to 1.0 × 10 11 e/m 3 . Conjunctions with mid‐latitude ionosondes demonstrate that E‐CHAIM's errors appear to result from compounding same‐signed errors in its NmF2, hmF2, and topside thickness at these latitudes. Overall, E‐CHAIM exhibits strong performance within the polar cap and auroral oval but performs comparably to the IRI at sub‐auroral latitudes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".