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Record W7127070733 · doi:10.1051/medsci/2025036/pdf

Prix Nobel de physique 2024 : John J. Hopfield et Geoffrey E. Hinton

2025· article· fr· W7127070733 on OpenAlex
Alaedine Benani

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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueSpringer Link (Chiba Institute of Technology) · 2025
Typearticle
Languagefr
FieldMaterials Science
TopicMachine Learning in Materials Science
Canadian institutionsnot available
Fundersnot available
KeywordsPoint (geometry)EffiNatural (archaeology)

Abstract

fetched live from OpenAlex

Le 8 octobre 2024, le prix Nobel de physique a été attribué à John J. Hopfield, professeur à l’université de Princeton (États-Unis), et à Geoffrey E. Hinton, professeur à l’université de Toronto (Canada), pour leurs « découvertes fondamentales ayant rendu possible l’apprentissage automatique au moyen de réseaux de neurones artificiels ». Le comité Nobel précise que John Hopfield a conçu une mémoire associative capable de stocker et de reconstituer des images, tandis que Geoffrey Hinton a mis au point une méthode permettant de réaliser des tâches telles que l’identification d’éléments particuliers au sein d’images. Cet article retrace le parcours de ces deux chercheurs et présente leurs contributions pionnières.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.504
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.003
Scholarly communication0.0000.001
Open science0.0030.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.010
GPT teacher head0.273
Teacher spread0.263 · 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