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Record W4399797158 · doi:10.1093/glycob/cwae040

HARRY SCHACHTER obituary

2024· article· en· W4399797158 on OpenAlexaffabout
Inka Brockhausen, Jim Dennis, Paul A. Gleeson, Kelley W. Moremen, Pamela Stanley

Bibliographic record

VenueGlycobiology · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGlycosylation and Glycoproteins Research
Canadian institutionsLunenfeld-Tanenbaum Research InstituteMount Sinai HospitalQueen's University
Fundersnot available
KeywordsObituaryPhilosophyTheology

Abstract

fetched live from OpenAlex

Well known glycobiologist Harry Schachter passed away on 2024 April 17 in Toronto, at the age of 91. He was the loving husband of his wife Judy, loving father of his children Asher and Aviva, and grandfather of Adam, Noah, Sarah and Audrey. Glycobiologists will miss him as a kind and humble person who constantly went out of his way to include everybody in his circle of colleagues with respect and acceptance. He always had the full and loving support of Judy. Harry Schachter was gifted with tremendous intellect and drive to discover the biochemical basis of diseases. He was a role model for his trainees, collaborators and colleagues. Harry was a great ambassador for the glycobiology field. At conferences, he invariably had probing questions for the speakers, and loved to contribute to the discussion after the sessions. He took notes and eagerly shared the meeting highlights with students and colleagues at the weekly Toronto Glycobiology meetings. With an outstanding scientific career, he pioneered the discovery of new enzymes and pathways in the biosynthesis of glycoproteins. He was a wonderful colleague and respected scientist, encouraging clarity and emphasizing honesty in scientific discourse. The world is enriched by Harry’s groundbreaking work that provided foundations for understanding the functions of glycoproteins in health and disease.

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)0.002
metaresearch head score (Gemma)0.011
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: none
Teacher disagreement score0.153
Threshold uncertainty score0.512

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.011
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0040.001
Scholarly communication0.0050.002
Open science0.0010.003
Research integrity0.0040.008
Insufficient payload (model declined to judge)0.1530.114

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 head0.009
GPT teacher head0.284
Teacher spread0.275 · 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
Published2024
Admission routes2
Has abstractyes

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