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Record W4401798991 · doi:10.1117/12.3019261

The far-infrared enhanced survey spectrometer (FIRESS) for PRIMA

2024· article· en· W4401798991 on OpenAlexaff
C. M. Bradford, Jason Glenn, M. Meixner, K. M. Pontoppidan, A. Kogut, Alexandra Pope, Willem Jellema, J. J. A. Baselmans, David Naylor, R. M. J. Janssen, Steven Hailey-Dunsheath, P. M. Echternach, Peter K. Day, H. G. LeDuc, Nicholas F. Cothard, Thomas R. Stevenson, M. C. Foote, C. D. Dowell, J. E. McGuire, Michael Rodgers, Thomas S. Pagano, Logan Foote, Elijah Kane, Chris Albert

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAstronomical Observations and Instrumentation
Canadian institutionsUniversity of Lethbridge
Fundersnot available
KeywordsSpectrometerFar infraredInfraredRemote sensingPhysicsOpticsGeology

Abstract

fetched live from OpenAlex

FIRESS is the multi-purpose spectrometer proposed for the PRobe far-Infrared Mission for Astrophysics (PRIMA). The sensitive spectrometer on the cold telescope provide factors of 1,000 to 100,000 improvement in spatial-spectral mapping speed relative to Herschel, accessing galaxies across the arc of cosmic history via their dust-immune far-infrared spectral diagnostics. FIRESS covers the 24 to 235 micron range with four slit-fed grating spectrometer modules providing resolving power between 85 and 130. The four slits overlap in pairs so that a complete spectrum of any object of interest is obtained in 2 pointings. For higher-resolving-power studies, a Fourier-transform module (FTM) is inserted into the light path in advance of the grating backends. The FTM serves all four bands and boosts the resolving power up to 4,400 at 112 microns, allowing extraction of the faint HD transition in protoplanetary disks. FIRESS uses four 2016-pixel arrays of kinetic inductance detectors (KIDs) which operate at the astrophysical photon background limit. KID sensitivities for FIRESS have been demonstrated, and environmental qualification of prototype arrays is underway.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.767
Threshold uncertainty score0.146

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 head0.016
GPT teacher head0.238
Teacher spread0.222 · 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 designOther design
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

Citations0
Published2024
Admission routes1
Has abstractyes

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