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How Important Are Brain Banks for Alcohol Research?

2003· article· en· W2011912760 on OpenAlexaff
Clive Harper, Therese Garrick, Izuru Matsumoto, Adolf Pfefferbaum, Elfar Adalsteinsson, Edith V. Sullivan, Peter R. Dodd, Joanne M. Lewohl, Roger F. Butterworth

Bibliographic record

VenueAlcoholism Clinical and Experimental Research · 2003
Typearticle
Languageen
FieldMedicine
TopicAlcoholism and Thiamine Deficiency
Canadian institutionsHôpital Saint-Luc
FundersNational Institute on Alcohol Abuse and Alcoholism
KeywordsBrain tissueNeurochemicalArtArt historyPsychoanalysisGerontologyNeuroscienceMedicinePsychology

Abstract

fetched live from OpenAlex

This article contains the proceedings of a symposium at the 2002 RSA/ISBRA Meeting in San Francisco, organized and chaired by Clive Harper and co-chaired by Izuru Matsumoto. The presentations were (1) Introduction, by Clive Harper; (2) The quality of tissue-a critical issue, by Therese Garrick; (3) The first systematic brain tissue donor program in Japan, by Izuru Matsumoto; (4) Brain scans after death-really! by Adolf Pfefferbaum, Elfar Adalsteinsson, and Edith Sullivan; (5) Capture that (genial) expression, by Joanne Lewohl and Peter Dodd; and (6) Neurochemical/pharmacological studies: experimental design and limitations, by Roger Butterworth.

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)0.014
metaresearch head score (Gemma)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
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 score0.236
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0140.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.395
GPT teacher head0.551
Teacher spread0.156 · 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.

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

Citations18
Published2003
Admission routes1
Has abstractyes

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