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Record W2494123586 · doi:10.1117/12.2232641

Canadian contributions studies for the WFIRST instruments

2016· article· en· W2494123586 on OpenAlexaffabout
Jean‐François Lavigne, Neil Rowlands, Frédéric Grandmont, Darquise Lafrenière, Christian Marois, Olivier Daigle, Simon Thibault, D. Schade, Étienne Artigau, Denis Brousseau, Jérôme Maîre, G. Cretot-Richert, Marie-Noëlle Ducharme, Luc Lévesque, Denis Laurin, J. Dupuis

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2016
Typearticle
Languageen
FieldEngineering
TopicCalibration and Measurement Techniques
Canadian institutionsUniversity of TorontoCanadian Space AgencyUniversité LavalUniversité de MontréalHoneywell (Canada)Herzberg Institute of AstrophysicsABB (Canada)
Fundersnot available
KeywordsComputer scienceRemote sensingGeology

Abstract

fetched live from OpenAlex

WFIRST-AFTA is the NASA’s highest ranked astrophysics mission for the next decade that was identified in the New World, New Horizon survey. The mission scientific drivers correspond to some of the deep questions identified in the Canadian LRP2010, and are also of great interest for the Canadian scientists. Given that there is also a great interest in having an international collaboration in this mission, the Canadian Space Agency awarded two contracts to study a Canadian participation in the mission, one related to each instrument. This paper presents a summary of the technical contributions that were considered for a Canadian contribution to the coronagraph and wide field 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)0.014
metaresearch head score (Gemma)0.020
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: Not applicable
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.079
Threshold uncertainty score0.572

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.020
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0080.009
Science and technology studies0.0140.002
Scholarly communication0.0080.003
Open science0.0030.004
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0260.005

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 head0.018
GPT teacher head0.244
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 designNot applicable
Domainnot available
GenreMethods

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
Published2016
Admission routes2
Has abstractyes

Explore more

Same venueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIESame topicCalibration and Measurement TechniquesFrench-language works237,207