The Empirical Canadian High Arctic Ionospheric Model (E‐CHAIM): <i>N</i><sub><i>m</i></sub><i>F</i><sub>2</sub> and <i>h</i><sub><i>m</i></sub><i>F</i><sub>2</sub>
Bibliographic record
Abstract
Abstract We present here the Empirical Canadian High Arctic Ionospheric Model (E‐CHAIM) quiet N m F 2 , perturbation N m F 2 , and quiet h m F 2 models. These models provide peak ionospheric characteristics for a domain above 50°N geomagnetic latitude. Model fitting is undertaken using all available ionosonde and radio occultation electron density data, constituting a data set of over 28 million observations. A comprehensive validation of the model is undertaken, and performance is compared to that of the International Reference Ionosphere (IRI). In the case of the quiet N m F 2 model, the E‐CHAIM model provides a systematic improvement over the IRI Union Radio Scientifique Internationale maps. At all stations within the polar cap, we see drastic RMS error improvements over the IRI by up to 1.3 MHz in critical frequency (up to 60% in N m F 2 ). These improvements occur primarily during equinox periods and at low solar activities, decreasing somewhat as one tends to lower latitudes. Qualitatively, the E‐CHAIM is capable of representing auroral enhancements in N m F 2 , as well as the location and extent of the main ionospheric trough, not reproduced by the IRI. The included N m F 2 storm model demonstrates improvements over the IRI by up to 35% and over the quiet time E‐CHAIM model by up to 30%. In terms of h m F 2 , over the validation periods used in this study, we found overall RMS errors of ~13 km for E‐CHAIM, with IRI2007 overall h m F 2 errors ranging between 16 km and 22 km. The E‐CHAIM performs comparably to or slightly better than the IRI within the polar cap; however, significant improvements are found within the auroral oval.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".