Data‐derived spatiotemporal resolution constraints for global auroral imagers
Bibliographic record
Abstract
We present new data‐derived constraints on spatiotemporal resolution of global auroral imagers. The reported results are based on an extensive set of images from two previously flown instruments, POLAR UVI and IMAGE WIC, processed using the event detection methodology developed by Uritsky et al. (2002, 2003, 2006). We use the cross‐scale analysis of ground‐based and spacecraft observations of auroral emission regions by Kozelov et al. (2004) to derive the power law exponent relating spatial and temporal scales of auroral precipitation events, and estimate the normalization factor entering this relation using the satellite data. Our results show the existence of a nontrivial scaling relation between the relaxation time and the spatial dimension of auroral emission events. We use this relation as a proxi to the resolution scaling function providing non‐redundant combinations of spatial and temporal resolution of an optimized auroral imager consistent with the dynamics of multiscale auroral precipitation.
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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 | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".