Radar efficiency and the calculation of decade-long PMSE
Bibliographic record
Abstract
Abstract. The Resolute Bay VHF radar, located in Nunavut, Canada (75.0 ◦ N, 95.0 ◦ W) and operating at 51.5 MHz, has been used to investigate Polar Mesosphere Summer Echoes (PMSE) since 1997. PMSE are a unique form of strong co-herent radar echoes, and their understanding has been a chal-lenge to the scientific community since their discovery more than three decades ago. While other high latitude radars have recorded strong levels of PMSE activities, the Resolute Bay radar has observed relatively lower levels of PMSE strengths. In order to derive absolute measurements of PMSE strength at this site, a technique is developed to determine the radar efficiency using cosmic (sky) noise variations along with the help of a calibrated noise source. VHF radars are only rarely calibrated, but determination of efficiency is even less com-mon. Here we emphasize the importance of efficiency for determination of cross-section measurements. The signifi-cant advantage of this method is that it can be directly ap-plied to any MST radar system anywhere in the world as long as the sky noise variations are known. The radar ef-ficiencies for two on-site radars at Resolute Bay are deter-mined. PMSE backscatter cross-section is estimated, and decade-long PMSE strength variations at this location are in-vestigated. It was noticed that the median of the backscatter cross-section distribution remains relatively unchanged, but over the years a great level of variability occurs in the high power tail of the distribution.
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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 | 0.004 |
| 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 | 0.000 | 0.001 |
| 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".