Tracking of polar cap ionospheric patches using data assimilation
Bibliographic record
Abstract
Ionospheric F‐region patches are 2–10 larger than background electron densities in the polar ionosphere. The EISCAT Svalbard incoherent radar (ESR) observed a sequence of patches between 2000–2200 UT on 12 December 2001. In this paper the source of these structures is investigated using several other data sets, together with a convection‐driven trajectory analysis. The data are assimilated into Ionospheric Data Assimilation Three Dimensional (IDA3D). The background model used is the National Center for Atmospheric Research‐Thermosphere Ionosphere Mesosphere Electrodynamics General Circulation Model (NCAR TIMEGCM). The trajectory analysis is based on maps of ionospheric convection obtained from the Assimilative Mapping of Ionospheric Electrodynamics (AMIE). In addition to patches, a tongue of ionization (TOI) is investigated. It is shown that patches formed part of the TOI. It is tempting to conclude the TOI and the patches originate at midlatitudes. However, the IDA3D and trajectory analysis suggest that they were transported toward noon from the morning and afternoon sectors near 62° geographic latitude. Thus for this case the TOI and patches did not originate at midlatitudes. This work represents advances in the field of patch research. A new capability to perform an analysis of patch origin and fate, using three‐dimensional (3‐D) ionospheric assimilation and 2‐D trajectory analysis codes, is demonstrated for a sequence of patches observed 12 December 2001. The current resolution of the technique is not able to identify detailed patch formation mechanisms. However, by it can track the plasma back in time to locations and times where patch formation mechanisms operate.
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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 | 0.000 | 0.001 |
| 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) | 0.000 | 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".