Review of Environmental Monitoring by Means of Radio Waves in the Polar Regions: From Atmosphere to Geospace
Bibliographic record
Abstract
Abstract The Antarctic and Arctic regions are Earth's open windows to outer space. They provide unique opportunities for investigating the troposphere–thermosphere–ionosphere–plasmasphere system at high latitudes, which is not as well understood as the mid- and low-latitude regions mainly due to the paucity of experimental observations. In addition, different neutral and ionised atmospheric layers at high latitudes are much more variable compared to lower latitudes, and their variability is due to mechanisms not yet fully understood. Fortunately, in this new millennium the observing infrastructure in Antarctica and the Arctic has been growing, thus providing scientists with new opportunities to advance our knowledge on the polar atmosphere and geospace. This review shows that it is of paramount importance to perform integrated, multi-disciplinary research, making use of long-term multi-instrument observations combined with ad hoc measurement campaigns to improve our capability of investigating atmospheric dynamics in the polar regions from the troposphere up to the plasmasphere, as well as the coupling between atmospheric layers. Starting from the state of the art of understanding the polar atmosphere, our survey outlines the roadmap for enhancing scientific investigation of its physical mechanisms and dynamics through the full exploitation of the available infrastructures for radio-based environmental monitoring.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".