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Record W2329652225 · doi:10.1097/fbp.0b013e32834dbbb1

Learning impairment by Δ9-tetrahydrocannabinol in adolescence is attributable to deficits in chunking

2011· article· en· W2329652225 on OpenAlexfundno aff
Ryan Steel, John H. Miller, Dalice Sim, Darren J. Day

Bibliographic record

VenueBehavioural Pharmacology · 2011
Typearticle
Languageen
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsnot available
FundersVictoria UniversityUniversity of Victoria
KeywordsChunking (psychology)CannabisTetrahydrocannabinolΔ9-tetrahydrocannabinolDronabinolDrugMedicinePsychologyDevelopmental psychologyNeuroscienceAudiologyPharmacologyPsychiatryInternal medicineCannabinoidCognitive psychology

Abstract

fetched live from OpenAlex

Cannabis is the most popular illicit drug used by adolescents. Yet, there are only a few studies that have examined the effects of cannabis use on learning and memory during this sensitive and important neurodevelopmental stage. Male adolescent Sprague-Dawley rats were treated with Δ(9)-tetrahydrocannabinol (THC, 6 mg/kg) daily for 27 days and concurrently trained in a spatial learning and memory task. The chronic effects of cannabis use were specifically examined by assessing animal behaviour during the 'postacute' period (17 h after drug exposure), when minimal acute drug burden is expected to be present. The postacute period is a good model for cannabis use patterns in human adolescents. In addition, we investigated whether the hierarchical organization of working memory (chunking) was impaired by THC-treatment. We show that THC exposure impairs adolescent learning when tested in the postacute period, and that THC impairs the ability of animals to use a chunking strategy.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.001
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 head0.046
GPT teacher head0.331
Teacher spread0.285 · 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 designObservational
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

Citations9
Published2011
Admission routes1
Has abstractyes

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