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Record W2160672673 · doi:10.1037//1064-1297.10.3.162

Applying laboratory research: Drug anticipation and the treatment of drug addiction.

2002· review· en· W2160672673 on OpenAlex
Shepard Siegel, Barbara M. C. Ramos

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueExperimental and Clinical Psychopharmacology · 2002
Typereview
Languageen
FieldNeuroscience
TopicMemory and Neural Mechanisms
Canadian institutionsMcMaster University
FundersNational Institute on Drug Abuse
KeywordsAddictionDrugAnticipation (artificial intelligence)Drug withdrawalPsychologyDrug toleranceMedicineDrug addictPsychiatry

Abstract

fetched live from OpenAlex

Basic research concerning drug tolerance and withdrawal may inform clinical practice, and vice versa. Three areas that integrate the work of the laboratory and the clinic are discussed: (a) drug overdose, (b) cue exposure treatment of addiction, and (c) pharmacological treatment of withdrawal symptoms. The areas are related in that they indicate the contribution of drug-paired cues to the effects of addictive drugs and the role of Pavlovian conditioning of drug effects in drug tolerance and withdrawal symptoms.

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)0.001
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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.986
Threshold uncertainty score0.714

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.347
GPT teacher head0.547
Teacher spread0.200 · 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