Ground‐based optical determination of the b2i boundary: A basis for an optical MT‐index
Bibliographic record
Abstract
The equatorward boundary of the proton aurora corresponds to a transition from strong pitch angle scattering to bounce trapped particles. This transition has been identified as the b2i boundary in Defense Meteorological Satellite Program (DMSP) ion data [ Newell et al. , 1996 ]. We use ion data from 29 DMSP overflights of the Canadian Auroral Network for the OPEN Program Unified Study (CANOPUS) Meridian Scanning Photometer (MSP) located at Gillam, Canada, to develop a simple algorithm to identify the b2i boundary in latitude profiles of proton auroral (486 nm) brightness. Applying this algorithm to a ten year set of Gillam MSP data, we obtain ∼250,000 identifications of the “optical b2i,” the magnetic latitude of which we refer to as b2i Λ . We intercompare ∼1600 near‐simultaneous optical and in situ b2i Λ , concluding that the optical b2i Λ is a reasonable basis for an optical equivalent to the MT‐index put forward by Sergeev and Gvozdevsky [1995] . Using ∼17,000 simultaneous measurements, we demonstrate a strong correlation between the optical b2i Λ and the inclination of the magnetic field as measured at GOES 8. We develop an empirical model for predicting the GOES 8 inclination, given the universal time, dipole tilt, and the optical b2i Λ , as determined at Gillam. We also show that in terms of information content, the b2i boundary is an optimal boundary upon which to base such an empirical model.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".