A statistical model of high latitude sporadic-E climatology for E-CHAIM
Bibliographic record
Abstract
Sporadic-E, thin metallic ion layers in the lower ionosphere compressed via neutral wind shears or externally imposed electric fields, pose considerable challenges for High Frequency (HF) radio propagation modeling.As their name suggests, these layers can appear to be quasi-stochastic, requiring both an abundance of metallic ions and a mechanism through which to compress them into thin, dense layers; as such, modeling Sporadic-E has been a persistent and long-standing challenge in ionospheric modeling.With new physics-based models now beginning to develop the capability to capture the processes that produce these structures, we will here revisit empirical modeling of Sporadic-E and examine the capability of existing measurements to adequately constrain an empirical model.Using Radio Occultation (RO) measurements of these Sporadic-E layers from all available missions (3.5 million observations), we have constructed a probabilistic model of Sporadic-E, its altitude, and its intensity over high latitude regions.This presentation will provide an overview of this model and examine its performance via independent validation against both other RO and ground-based observations.We will furthermore examine the modeled behaviour and use the model to understand the climatological dynamics of Sporadic-E at high latitudes.Further discussion will explore how to implement this model within the existing Empirical Canadian High Arctic Ionospheric Model (E-CHAIM) [1,2,3].
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".