Eureka, 80° N, SKiYMET meteor radar temperatures compared with Aura MLS values
Bibliographic record
Abstract
Abstract. The meteor trail echo decay rates are analysed on-site to provide daily temperatures near 90 km. In order to get temperatures from trail decay times, either knowledge of the pressure or the background temperature height gradient near 90 km is required (Hocking, 1999). Hocking et al. (2004) have developed an empirical 90 km temperature gradient model depending only on latitude and time of year, which is used in the SKiYMET on-site meteor temperature analysis. Here we look at the sensitivity of the resulting temperature to the assumed gradient and compare it and the temperatures with daily AuraMLS averages near Eureka. Generally there is good agreement between radar and satellite for winter temperatures and their short-term variations. However there is a major difference in mid-summer both in the temperatures and the gradients. Increased turbulence in summer, which may overwhelm the ambipolar diffusion even at 90 km, is likely a major factor. These differences are investigated by generating ambipolar-controlled decay times from satellite pressure and temperature data at a range of heights and comparing with radar measurements. Our study suggests it may be possible to use these data to estimate eddy diffusion coefficients at heights below 90 km. Finally the simple temperature analysis (using satellite pressures), and a standard meteor wind analysis are used to compare mean diurnal variations of temperature (T) with those of zonal wind (U) and meridional wind (V) in composite multi-year monthly intervals.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".