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Record W3173941778 · doi:10.5281/zenodo.3755958

Science, Technical and Strategic benefits of Canadian partnership with Subaru

2019· article· en· W3173941778 on OpenAlexaffabout
Marcin Sawicki, Ivana Damjanov, Stéphane Courteau, H. K. C. Yee, Bob Abraham, Jo Bovy, M. R. Drout, Suresh Sivanandam, Dae‐Sik Moon, Adam Muzzin, Laura C. Parker, Will Percival, J. Cami, Chris O'Dea, Jeremy Heyl, Ludo van Waerbeke, Sara Fisher Ellison, J. P. Willis, Kim A. Venn, Colin Bradley, Tracy Webb, Alan W. McConnachie, Christian Marois, J. J. Kavelaars, Laura Ferrarese, P. Côté, Stephen Gwyn, Luc Simard, René Doyon, David Lafreniere

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2019
Typearticle
Languageen
FieldMedicine
TopicBiotechnology and Related Fields
Canadian institutionsMcGill UniversityUniversity of British ColumbiaQueen's UniversityMcMaster UniversityUniversity of Waterloo
Fundersnot available
KeywordsGeneral partnershipBusinessPolitical scienceComputer science

Abstract

fetched live from OpenAlex

Recent developments have opened up an opportunity to join the Subaru telescope under favourable terms. With the demonstrated success of SCExAO and HSC, and first light of the PFS nearly upon us, the immediate prospects for Subaru to make breakthrough discoveries that align well with Canadian interests are excellent. In this white paper we describe the scientific and technical (instrumentation) opportunities that a partnership with Subaru would enable. IWe highlight the important synergies with TMT, MSE and Euclid, and how a relatively small investment in Subaru can significantly enhance our ability to take advantage of those facilities. In particular, access to PFS in the decade before MSE sees first light will be critical for ensuring Canadians have the technical and scientific expertise to fully exploit, and lead, science with that next generation spectroscopic facility. The partnership also has strategic value, as it represents an important step toward building an alliance of observatories on Maunakea, which itself has the potential for dramatically increasing scientific impact and reducing costs.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.930
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.035
GPT teacher head0.236
Teacher spread0.201 · 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.

Study designNot applicable
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
Published2019
Admission routes2
Has abstractyes

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