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DNA Barcoding Methods for Land Plants

2012· article· en· W177663743 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.

Bibliographic record

VenueMethods in molecular biology · 2012
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic diversity and population structure
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsDNA barcodingBiologyBarcodeCladeEvolutionary biologyDNA extractionEnvironmental DNAInternal transcribed spacerDNA sequencingComputational biologyDNAPolymerase chain reactionEcologyGeneticsGenePhylogeneticsBiodiversityComputer sciencePhylogenetic tree

Abstract

fetched live from OpenAlex

DNA barcoding in the land plants presents a number of challenges compared to DNA barcoding in many animal clades. The CO1 animal DNA barcode is not effective for plants. Plant species hybridize frequently, and there are many cases of recent speciation via mechanisms, such as polyploidy and breeding system transitions. Additionally, there are many life-history trait combinations, which combine to reduce the likelihood of a small number of markers effectively tracking plant species boundaries. Recent results, however, from the two chosen core plant DNA barcode regions rbcL and matK plus two supplementary regions trnH-psbA and internal transcribed spacer (ITS) (or ITS2) have demonstrated reasonable levels of species discrimination in both floristic and taxonomically focused studies. We describe sampling techniques, extraction protocols, and PCR methods for each of these two core and two supplementary plant DNA barcode regions, with extensive notes supporting their implementation for both low- and high-throughput facilities.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.176
Threshold uncertainty score0.645

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.043
GPT teacher head0.419
Teacher spread0.376 · 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