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Rapid and reliable high‐throughput methods of DNA extraction for use in barcoding and molecular systematics of mushrooms

2009· article· en· W2122184569 on OpenAlex

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueMolecular Ecology Resources · 2009
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMycorrhizal Fungi and Plant Interactions
Canadian institutionsRoyal Ontario MuseumUniversity of Toronto
FundersOntario Genomics InstituteGenome Canada
KeywordsBiologyDNA barcodingSystematicsDNA extractionEvolutionary biologyExtraction (chemistry)Computational biologyZoologyGeneticsTaxonomy (biology)GenePolymerase chain reactionChromatography

Abstract

fetched live from OpenAlex

We present two methods for DNA extraction from fresh and dried mushrooms that are adaptable to high-throughput sequencing initiatives, such as DNA barcoding. Our results show that these protocols yield ∼85% sequencing success from recently collected materials. Tests with both recent (<2 year) and older (>100 years) specimens reveal that older collections have low success rates and may be an inefficient resource for populating a barcode database. However, our method of extracting DNA from herbarium samples using small amount of tissue is reliable and could be used for important historical specimens. The application of these protocols greatly reduces time, and therefore cost, of generating DNA sequences from mushrooms and other fungi vs. traditional extraction methods. The efficiency of these methods illustrates that standardization and streamlining of sample processing should be shifted from the laboratory to the field.

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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.184
Threshold uncertainty score0.214

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.018
GPT teacher head0.265
Teacher spread0.247 · 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