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Record W4413115982 · doi:10.1007/s11214-025-01199-x

The Tandem Reconnection and Cusp Electrodynamics Reconnaissance Satellites (TRACERS) Science Operations Center

2025· article· en· W4413115982 on OpenAlexaff
I. W. Christopher, C. A. Kletzing, D. Crawford, Chris Piker, DL Wilkinson, Kim Steele, Steve Petrinec, S. R. Bounds, Steven Vaclavik, Saad Omar, E. O. Shul'Ts, Michael Winter, David M. Miles

Bibliographic record

VenueSpace Science Reviews · 2025
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstro and Planetary Science
Canadian institutionsNexen (Canada)
FundersGoddard Space Flight CenterNational Aeronautics and Space Administration
KeywordsSpacecraftEphemerisSpace SciencePhysicsScientific instrumentCitizen scienceData processingRemote sensingComputer scienceSystems engineeringAerospace engineeringAstronomySatelliteDatabaseEngineeringGeography

Abstract

fetched live from OpenAlex

The primary purpose of the Tandem Reconnection And Cusp Electrodynamics Reconnaissance Satellites (TRACERS) Science Operations Center (SOC) is to ensure that the data necessary to achieve the TRACERS science goals are acquired, processed, and distributed to the scientific community. The SOC role in data acquisition is to facilitate science instrument planning and operations, through a weekly commanding cycle. Data processing includes generation of Level 0 and Level 1 data products, creation of Spacecraft Planet Instrument Camera-matrix Events (SPICE) kernels to provide spacecraft ephemerides and coordinate transforms for the mission, and ensuring consistency of all Level 2+ products produced by the individual instrument teams. Data distribution is undertaken in two ways. First, by hosting TRACERS data products on a public web portal during the active mission, and second by preparing mission data for transfer to the Space Physics Data Facility (SPDF) for long-term archiving.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.025
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0120.004

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.

Opus teacher head0.009
GPT teacher head0.277
Teacher spread0.268 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations2
Published2025
Admission routes1
Has abstractyes

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