History of the Super Dual Auroral Radar Network (SuperDARN)-I: pre-SuperDARN developments in high frequency radar technology for ionospheric research and selected scientific results
Bibliographic record
Abstract
Part I of this history describes the motivations for developing radars in the high frequency (HF) band to study plasma density irregularities in the F region of the auroral zone and polar cap ionospheres. French and Swedish scientists were the first to use HF frequencies to study the Doppler velocities of HF radar backscatter from F-region plasma density irregularities over northern Sweden. These observations encouraged the author of this paper to pursue similar measurements over northeastern Alaska, and this eventually led to the construction of a large HF-phased-array radar at Goose Bay, Labrador, Canada. This radar utilized frequencies from 8–20 MHz and could be electronically steered over 16 beam directions, covering a 52 ∘ azimuth sector. Subsequently, similar radars were constructed at Schefferville, Quebec, and Halley Station, Antarctica. Observations with these radars showed that F-region backscatter often exhibited Doppler velocities that were significantly above and below the ion-acoustic velocity. This distinguished HF Doppler measurements from prior measurements of E-region irregularities that were obtained with radars operating at very high frequency (VHF) and ultra-high frequency (UHF). Results obtained with these early HF radars are also presented. They include comparisons of Doppler velocities observed with HF radars and incoherent scatter radars, comparisons of plasma convection patterns observed simultaneously in conjugate hemispheres, and the response of these patterns to changes in the interplanetary magnetic field, transient velocity enhancements in the dayside cusp, preferred frequencies for geomagnetic pulsations, and observations of medium-scale atmospheric gravity waves with HF radars.
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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".