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Record W51052374 · doi:10.1520/jfs2001207

A Simple DNA Extraction Method for Marijuana Samples Used in Amplified Fragment Length Polymorphism (AFLP) Analysis

2003· article· en· W51052374 on OpenAlexaff
Heather Miller Coyle, Gary Shutler, Stephen L. Abrams, J Hanniman, S Neylon, Carll Ladd, Timothy M. Palmbach, Hsin‐Chen Lee

Bibliographic record

VenueJournal of Forensic Sciences · 2003
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPlant tissue culture and regeneration
Canadian institutionsRoyal Canadian Mounted Police
Fundersnot available
KeywordsAmplified fragment length polymorphismDNA extractionDNAPolymerase chain reactionBiologyDNA profilingChromatographyMolecular biologyGeneticsChemistryGeneMedicine

Abstract

fetched live from OpenAlex

As a first step in developing a molecular method for the individualization of marijuana samples, we evaluated a plant DNA extraction kit. The QIAGEN plant DNeasy method uses a spin column format for recovery of DNA and is effective for obtaining high molecular weight DNA from leaf, flower (bud), and seed samples of marijuana. The average DNA yield was 125-500 ng per 100 milligrams of fresh plant tissue. The recovered DNA was of polymerase chain reaction (PCR) quality as measured by the ability to generate reproducible amplified fragment length polymorphism (AFLP) profiles. AFLP is a technique used to create a DNA profile for plant varieties and is being applied to marijuana samples by the authors to link growers and distributors of clonal material. The QIAGEN plant DNeasy method was simple, efficient, and reproducible for processing small quantities of marijuana into DNA.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.007

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 head0.038
GPT teacher head0.330
Teacher spread0.292 · 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 designBench or experimental
Domainnot available
GenreMethods

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

Citations40
Published2003
Admission routes1
Has abstractyes

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