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Record W2424340364 · doi:10.1139/g10-094

Identification of Capsicum species using SNP markers based on high resolution melting analysis

2010· article· en· W2424340364 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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueGenome · 2010
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Pathogenic Bacteria Studies
Canadian institutionsnot available
Fundersnot available
KeywordsHigh Resolution MeltBiologySingle-nucleotide polymorphismGermplasmIntergenic regionGenetic markerGeneticsSNPGenotypeGeneBotanyGenome

Abstract

fetched live from OpenAlex

Single nucleotide polymorphisms (SNPs) derived from both nuclear and cytoplasmic DNA sequences were developed to identify distinct species of Capsicum. Species identification was achieved by detecting allelic variations of these type of markers via high resolution melting analysis (HRM). We used the HRM polymorphisms of COSII markers and the Waxy gene from the nuclear sequence, in addition to the intergenic spacer between trnL and trnF from cytoplasmic DNA as our SNP markers. A total of 31 accessions of Capsicum, representing six species, were analyzed using this method. As single markers were insufficient for identifying Capsicum species, combinations of all markers unambiguously identified all six. A phylogeny based on the SNP markers was consistent with the current taxonomy of Capsicum species. These observations demonstrate that the markers developed in this study are useful for rapid identification of new germplasm for management of Capsicum species.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.979
Threshold uncertainty score0.236

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.027
GPT teacher head0.217
Teacher spread0.190 · 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