A multi-instrument approach to mapping the global dayside merging rate
Bibliographic record
Abstract
Abstract. For the first time three different methods have been used to calculate the global merging rate during the same substorm growth phase. The ionospheric plasma drift was monitored by six of the Northern Hemisphere SuperDARN radars, allowing the convection pattern to be studied over 12 h of magnetic local time. The radars observed reconnection signatures on the dayside simultaneously with substorm signatures on the nightside. The three methods to calculate the global merging rate are: (i) the equatorward expansion of radar backscatter on the nightside, which provides an estimate of the rate of polar cap expansion, while upstream WIND measurements gave an estimate of the reconnection electric fields; (ii) the derivation of the dayside boundary normal plasma flow velocity and an estimate of the extent of the ionospheric merging gap, from radar observation of dayside reconnection; (iii) utilizing the map-potential technique to map the high-latitude plasma flow and cross polar cap potential (Ruohoniemi and Baker, 1998), allowing the global dayside merging rate to be calculated. The three methods support an extensive magnetopause X-line length of between 30 ± 12RE and 35 ± 15 RE (assuming a single X-line and constant merging rate). Such close agreement between the different methods of calculation are unexpected, especially as the length of the magnetopause X-line is not well known.Key words. Magnetospheric physics (magnetopause, cusp and boundary layers; magnetosphere – ionosphere interactions; solar-wind magnetosphere interactions)
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".