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Record W2968723729 · doi:10.1145/3357514.3357515

EuroGP 2019 panel discussion

2019· article· en· W2968723729 on OpenAlex
Ting Hu, Lukáš Sekanina

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueACM SIGEVOlution · 2019
Typearticle
Languageen
FieldHealth Professions
TopicArtificial Intelligence in Healthcare
Canadian institutionsQueen's University
Fundersnot available
KeywordsThe InternetWorld Wide WebComputer scienceEveryday lifeSet (abstract data type)Synonym (taxonomy)Internet privacyPolitical science

Abstract

fetched live from OpenAlex

While the Internet existed as far back as the 1960s, it was not until the advent of the World Wide Web in the early 90s, that this technology began impacting our everyday lives as an information dissemination tool. This application is so dominant that many would treat the Web as the synonym of the Internet. A technique only takes off when it finds a vehicle to communicate with the wide audience, i.e., through a killer application. What is the killer application that will make GP become a part of our everyday lives? What are the application problems that GP is particularly suitable for? What can set GP apart from other learning algorithms, and allow it to stand out in the next waves of AI research?

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.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.372
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.038

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 head0.159
GPT teacher head0.454
Teacher spread0.294 · 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