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Record W2501136706 · doi:10.1098/rsbm.2016.0022

David Hunter Hubel. 27 February 1926 — 22 September 2013

2016· article· en· W2501136706 on OpenAlexfundno aff
Robert H. Wurtz

Bibliographic record

VenueBiographical Memoirs of Fellows of the Royal Society · 2016
Typearticle
Languageen
FieldNeuroscience
TopicVisual perception and processing mechanisms
Canadian institutionsnot available
FundersNational Academy of SciencesMcGill UniversityJohns Hopkins University
KeywordsNeuroscienceCognitive scienceVisual cortexCortex (anatomy)Computer sciencePsychology

Abstract

fetched live from OpenAlex

David Hunter Hubel was one of the great neuroscientists of the twentieth century. His experiments revolutionized our understanding of the brain mechanisms underlying vision. His 25-year collaboration with Torsten N. Wiesel revealed the beautifully ordered activity of single neurons in the visual cortex, how innate and learned factors shape its development, and how these neurons might be assembled to ultimately produce vision. Their work ushered in the current era of analyses of neurons at multiple levels of the cerebral cortex that seek to parse out the functional brain circuits underlying behaviour. For these achievements, Hubel and Wiesel, along with Roger W. Sperry, shared the Nobel Prize for Physiology or Medicine in 1981.

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)0.000
metaresearch head score (Gemma)0.001
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: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.149
Threshold uncertainty score0.500

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.1490.106

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 head0.025
GPT teacher head0.271
Teacher spread0.246 · 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

Citations1
Published2016
Admission routes1
Has abstractyes

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