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Record W4399784636 · doi:10.25518/0037-9565.11913

A Year-long Representation of the ILMT Observations in Different Coordinate Systems

2024· article· en· W4399784636 on OpenAlexafffund
Monalisa Dubey, Bhavya Ailawadhi, Talat Akhunov, E. F. Borra, Kuntal Misra, Naveen Dukiya, Jiuyang Fu, Baldeep Grewal, Paul Hickson, Brajesh Kumar, Vibhore Negi, Kumar Pranshu, Ethen Sun, Jean Surdej

Bibliographic record

VenueBulletin de la Société Royale des Sciences de Liège · 2024
Typearticle
Languageen
FieldEngineering
TopicGNSS positioning and interference
Canadian institutionsUniversity of British ColumbiaUniversité Laval
FundersNatural Sciences and Engineering Research Council of CanadaService Public de WallonieUniversité de LiègeBelgian Federal Science Policy OfficeFonds De La Recherche Scientifique - FNRSDepartment of Science and Technology, Ministry of Science and Technology, IndiaYork University
KeywordsRepresentation (politics)Coordinate systemGeologyComputer scienceMathematicsGeodesyArtificial intelligencePolitical science

Abstract

fetched live from OpenAlex

The 4m International Liquid Mirror Telescope (ILMT) is the first optical survey telescope in India that performs zenithal observations of a 22′ wide strip of the sky. To determine the portion of the sky covered by the ILMT during the entire year, we represent the ILMT Field of View (FoV) in three different coordinate systems - galactic, ecliptic, and equatorial. We adopt a constant declination of +29°21′41.4′′ and varying right ascension (RA) ranges corresponding to the Local Sidereal Time (LST). The observations from June to September are hampered due to the monsoon season. The handiness of such representations will allow us to locate a transient event in the ILMT FoV. This will enable prompt follow-up observations with other facilities.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.133
Threshold uncertainty score0.245

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.038
GPT teacher head0.302
Teacher spread0.264 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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
Published2024
Admission routes2
Has abstractyes

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