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Record W3020915492 · doi:10.48550/arxiv.1407.0026

The Evryscope: the first full-sky gigapixel-scale telescope

2014· article· en· W3020915492 on OpenAlexaboutno aff
Nicholas M. Law, O. Fors, Philip J. Wulfken, Jeffrey K. Ratzloff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicHistory and Developments in Astronomy
Canadian institutionsnot available
Fundersnot available
KeywordsSkyTelescopeScale (ratio)Full scaleRemote sensingAstronomyGeologyComputer sciencePhysicsCartographyGeographyComputer vision

Abstract

fetched live from OpenAlex

Current time-domain wide-field sky surveys generally operate with few-degree-sized fields and take many individual images to cover large sky areas each night. We present the design and project status of the Evryscope ("wide-seer"), which takes a different approach: using an array of 7cm telescopes to form a single wide-field-of-view pointed at every part of the accessible sky simultaneously and continuously. The Evryscope is a gigapixel-scale imager with a 9060 sq. deg. field of view and has an etendue three times larger than the Pan-STARRS sky survey. The system will search for transiting exoplanets around bright stars, M-dwarfs and white dwarfs, as well as detecting microlensing events, nearby supernovae, and gamma-ray burst afterglows. We present the current project status, including an update on the Evryscope prototype telescopes we have been operating for the last three years in the Canadian High Arctic.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.193
Threshold uncertainty score0.385

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.003

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 head0.004
GPT teacher head0.196
Teacher spread0.192 · 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

Citations0
Published2014
Admission routes1
Has abstractyes

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