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Record W4402789206 · doi:10.1098/rsbm.2024.0017

Endel Tulving. 26 May 1927—11 September 2023

2024· article· en· W4402789206 on OpenAlexaffabout
Fergus I. M. Craik FRS

Bibliographic record

VenueBiographical Memoirs of Fellows of the Royal Society · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicCephalopods and Marine Biology
Canadian institutionsBaycrest HospitalEnvironment and Climate Change Canada
FundersRoyal Society
KeywordsComputer science

Abstract

fetched live from OpenAlex

Abstract Endel Tulving was an Estonian–Canadian cognitive psychologist and cognitive neuroscientist who transformed the study of human memory in the 50 years between 1960 and 2010. He was born in Estonia in 1927, and spent several years in post-war Europe before emigrating to Canada in the late 1940s. He obtained degrees at the University of Toronto and at Harvard University before returning to the University of Toronto as a faculty member in 1956. He was always an independent thinker, and rejected the dominant view of memory and learning in terms of associations in favour of a more cognitive approach in which the organization of remembered events played a major part. He also developed theories on how events are encoded and retrieved, first in terms of behaviour and then in terms of their underlying neural correlates in specific brain regions. His theoretical ideas on different memory systems and their relations to different types of consciousness, laid out in his 1983 book Elements of episodic memory (Oxford University Press), continue to play a major part in current work on human memory. As a person, he retained a somewhat European sense of propriety and formality, which often made him seem quite formidable to new acquaintances. Once someone's scientific seriousness was established, however, Endel was warmly supportive of his students and colleagues. He was much loved by his family, and held in high esteem by his fellow scientists.

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 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)0.000
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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.317
Threshold uncertainty score0.664

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
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)0.0010.000

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 head0.011
GPT teacher head0.221
Teacher spread0.210 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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
Published2024
Admission routes2
Has abstractyes

Explore more

Same venueBiographical Memoirs of Fellows of the Royal SocietySame topicCephalopods and Marine BiologyFrench-language works237,207