Emerging Trends in Radar: Long-Range Surveillance in North Polar Region
Bibliographic record
Abstract
Sky-wave over-the-horizon radar (OTHR) relies on bouncing radio waves off the ionosphere to achieve long-range surveillance, even beyond the Earth's curvature. A critical component of OTHR is the real-time frequency management system (FMS), which must continuously adjust to accommodate the dynamic ionospheric conditions, particularly in high-latitude and polar regions. To maintain consistent detection of distant targets, OTHR systems must periodically adjust operating frequencies and elevation angles in response to these fluctuating conditions. In this context, the Assimilation Canadian High Arctic Ionospheric Model (A-CHAIM) was developed to represent the short-term variability and unique features of the high-latitude and polar ionosphere. This model serves as a cutting-edge tool for real-time ionospheric modeling in these challenging regions; however, inconsistent availability and distribution of observations ultimately limit the scales of structuring that the model can capture. To address these limitations, Defence Research and Development Canada is working on several enhancements to improve A-CHAIM's performance and reliability in the future.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 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".