The Swarm Langmuir Probe Ion Drift, Density and Effective Mass (SLIDEM) Product
Bibliographic record
Abstract
Abstract Current methods for estimating ion density on Swarm rely on the assumption of 100% O+ and no along-track ion velocity flows. These assumptions are routinely violated, particularly on the nightside and during high-latitude and polar cap traversals, compromising the accuracy of the measurements. The use of faceplate current data along with the Langmuir probe ion admittance measurements, and orbital-motion limited (OML) theory, make it possible to relax some of the assumptions inherent in current ESA Swarm density estimates. This further yields along-track ion drift and effective ion mass estimates. This paper describes the theoretical basis for estimating revised ion density, providing a new estimate for effective ion mass, as well as an alternative way of estimating along-track ion drift. The complete Swarm historical dataset has been generated and validated using empirical models (International Reference Ionosphere (IRI-2016), and Weimer (2005)), as well as ground-spacecraft conjunctions. Case studies and statistical results reveal clear geophysical signatures in the new product of light ions at low- and mid-latitudes and along-track ion drift at high latitudes, and their response to space weather.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".