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Record W3093168999 · doi:10.1364/cleopr.2020.c6g_2

Towards Integrated Astrophotonic Instruments for Exoplanet Biosignature Detection

2020· article· en· W3093168999 on OpenAlexaff
Ross Cheriton, A. Densmore, Suresh Sivanandam, Ernst de Mooij, Mohsen Kamandar Dezfouli, Daniele Melati, Rubin Ma, Shurui Wang, Dan‐Xia Xu, Jens H. Schmid, J. Lapointe, Pavel Cheben, Luc Simard, Siegfried Janz

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldChemistry
TopicSpectroscopy and Laser Applications
Canadian institutionsUniversity of TorontoHerzberg Institute of AstrophysicsNational Research Council Canada
Fundersnot available
KeywordsExoplanetRemote sensingAstrobiologyOpticsSpace (punctuation)Environmental sciencePhysicsAstronomyAerospace engineeringComputer sciencePlanetGeologyEngineeringOperating system

Abstract

fetched live from OpenAlex

We present the design of integrated astrophotonic instruments for high background atmospheric gas detection. Such instruments can be fibre-coupled to long focal length space telescopes as compact, flexure-free, instruments.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0030.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.013
GPT teacher head0.239
Teacher spread0.226 · 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 designBench or experimental
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

Citations1
Published2020
Admission routes1
Has abstractyes

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