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Record W4402980523 · doi:10.70537/ksdj4753

Application of Regulation (EU) 2017/745 of the European Parliament and of the Council to artificial intelligence

2024· article· pl· W4402980523 on OpenAlex
Monika Kupis

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

VenuePrzegląd Prawa Medycznego. · 2024
Typearticle
Languagepl
FieldSocial Sciences
TopicPolish Law and Legal System
Canadian institutionsUniversity of TorontoToronto Public Health
Fundersnot available
KeywordsEngineering

Abstract

fetched live from OpenAlex

Niniejsza publikacja przedstawia generalny kształt regulacji Rozporządzenia Parlamentu Europejskiego i Rady (UE) 2017/745 dedykowanej oprogramowaniu w ochronie zdrowia a stosowanej do oprogramowania AI. Porusza kwestie generalnej charakterystyki MDR względem oprogramowania, w tym definicji wyrobu medycznego w Rozporządzeniu 2017/745 względem sztucznej inteligencji, kryteria kwalifikacji oprogramowania AI jako wyrobu medycznego. Odnosi się również do problemów systemowych względem istniejących oraz projektowanych regulacji prawa bezwzględnie obowiązującego oraz soft law.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.853
Threshold uncertainty score0.888

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.063
GPT teacher head0.299
Teacher spread0.236 · 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