How Well Can We Estimate Pedersen Conductance From the THEMIS White‐Light All‐Sky Cameras?
Bibliographic record
Abstract
Abstract We show that a white‐light all‐sky imager can estimate Pedersen conductance with an uncertainty of 3 mho or 40%. Using a series of case studies over a wide range of geomagnetic activity, we compare estimates of Pedersen conductance from the backscatter spectrum of the Poker Flat Incoherent Scatter Radar with auroral intensities. We limit this comparison to an area bounding the radar measurements and within a limited area close to (but off) imager zenith. We confirm a linear relationship between conductance and the square root of auroral intensity predicted from a simple theoretical approximation. Hence, we extend a previous empirical result found for green‐line emissions to the case of white‐light off‐zenith emissions. The difference between the radar conductance and the best‐fit relationship has a mean of −0.76 ± 4.8 mho and a relative mean difference of 21% ± 78%. The uncertainties are reduced to −0.72 ± 3.3 mho and 0% ± 40% by averaging conductance over 10 min, which we attribute to the time that auroral features take to move across the imager field being greater than the 1‐min resolution of the radar data. Our results demonstrate and calibrate the use of Time History of Events and Macroscale Interactions during Substorms all‐sky imagers for estimating Pedersen conductance. This technique allows the extension of estimates of Pedersen conductance from Incoherent Scatter Radars to derive continental‐scale estimates on scales of ~1–10 min and ~100 km2. It thus complements estimates from low‐altitude satellites, satellite auroral imagers, and ground‐based magnetometers.
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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 | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".