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Record W2500252451 · doi:10.1385/0-89603-490-9:353

G Proteins and Mood Disorders

2003· book-chapter· en· W2500252451 on OpenAlexaff
Jun-Feng Wang, L. Tremor Young

Bibliographic record

VenueHumana Press eBooks · 2003
Typebook-chapter
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicReceptor Mechanisms and Signaling
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMonoaminergicSerotonergicNeuroscienceMood disordersMoodNeurotransmitter systemsNeurotransmitter receptorPsychologyDopaminergicCholinergicNeurotransmitterSerotoninReceptorBiologyMedicineInternal medicineDopaminePsychiatryCentral nervous system

Abstract

fetched live from OpenAlex

A neurobiological basis for mood disorders has long been postulated, but is yet to be conclusively established. Earlier studies on the monoaminergic neurotransmitter systems in mood disorder have been very suggestive, although not conclusive, of alterations in these systems (noradrenergic, dopaminergic, serotonergic, and cholinergic) possibly owing to changes in receptor sensitivity (Post and Ballenger, 1984). These data have resulted in a recent and relatively extensive field of research investigating mechanisms that regulate receptor responsivity, which has focused to a large extent on the G protein-coupled signal transduction pathways. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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)0.000
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: Review · Consensus signal: Review
Teacher disagreement score0.017
Threshold uncertainty score0.057

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0170.010

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 head0.021
GPT teacher head0.225
Teacher spread0.204 · 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
GenreReview

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

Citations0
Published2003
Admission routes1
Has abstractyes

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