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Record W7081434069

International Research Infrastucture Landscape 2019: A European Perspective

2022· other· en· W7081434069 on OpenAlexfundno aff

Bibliographic record

Venuenot available
Typeother
Languageen
FieldComputer Science
TopicGeochemistry and Geologic Mapping
Canadian institutionsnot available
FundersCollege of Pharmacy, University of MichiganCollege of Veterinary Medicine, Cornell UniversityNational Institutes of HealthResearch Institute of Economy, Trade and IndustryFudan UniversityPennsylvania State UniversityUniversity of TokyoEuropean CommissionPartnership for Advanced Computing in Europe AISBLUniversity of ChicagoUniversity of PennsylvaniaAustralian National UniversityUniversity of MichiganYork UniversityNational Science Foundation
KeywordsPerspective (graphical)Position (finance)Domain (mathematical analysis)HorizonEuropean unionInternational relations
DOInot available

Abstract

fetched live from OpenAlex

The book 'International Research Infrastucture Landscape 2019: A European Perspective' provides the final report of the RISCAPE-project, supported by the European Commission's Horizon 2020-project. The RISCAPE-project aims to provide a systematic, focused, high-quality, comprehensive, consistent and peer-reviewed international landscape analysis report on the position and complementarities of the major European RIs in the international Research Infrastructure landscape. University of Turku has contributed with the domain report on international Energy Research Infrastructures, which forms chapter 6 of the final book.

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)0.004
metaresearch head score (Gemma)0.002
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: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.020
Threshold uncertainty score0.066

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0050.013
Science and technology studies0.0020.003
Scholarly communication0.0160.010
Open science0.0010.004
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0200.006

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 head0.022
GPT teacher head0.296
Teacher spread0.273 · 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
GenreOther

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
Published2022
Admission routes1
Has abstractyes

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