Low-energy particle imaging on swarm and ePOP: A new view of the ionosphere
Bibliographic record
Abstract
Summary form only given. In-situ diagnostics of ionospheric plasma are made most commonly by instruments that provide only bulk properties such as electron density and temperature (by Langmuir probes), and ion temperature, flow velocity and composition (by retarding potential analyzers and ion drift meters). Instruments that measure full particle distribution functions (e.g. top-hat analyzers) typically do not function well in the ionosphere because of the low particle energies involved. Thermal Ion Imaging is a new technique that allows high-resolution, 2-D (angle-energy) imaging of plasma populations having characteristic energies in the range 0.1-100 eV. By using a charged-coupled device-based imaging detector, particle distributions are recorded with 64×64 pixel resolution, and at rates of up to 100 distribution images per second. Four TII-based instruments have been launched into orbit in the past year, three in November 2013 on the European Space Agency's Swarm satellites, and another, the Suprathermal Electron Imager, on Canada's Enhanced Polar Outflow Probe (ePOP) satellite launched in September 2013. This talk will describe the TII/SEI sensors and present results from their first year in orbit.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".