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Record W1999486817 · doi:10.1300/j301v03n03_06

An Overview of RAPD Analysis to Estimate Genetic Relationships in Lowbush Blueberry

2004· article· en· W1999486817 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

VenueSmall Fruits Review · 2004
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBerry genetics and cultivation research
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsRAPDBiologyGenetic similarityVacciniumSimilarity (geometry)DendrogramBotanyHorticultureGenetic diversityPopulationArtificial intelligence

Abstract

fetched live from OpenAlex

SUMMARY Randomly amplified polymorphic DNA (RAPD) analysis, a simple dominant molecular marker technique, has been used extensively for cultivar identification and relatedness studies in many perennial woody species. Thus, this technique should provide genetic information for lowbush blueberry (Vaccinium angustifolium Ait.). Young leaves of lowbush blueberry from field clones with varying phenotype were collected for DNA extraction. Pre-screening of RAPD primers resulted in 11 polymorphic primers and 140 consistent RAPD fragments. Eight primers were selected as useful for our study, the fragments scored and the data analyzed with Genstat5 to calculate similarity, produce dendrograms and perform a principal coordinate analysis. The RAPD analysis was able to identify distinct field clones. Average genetic similarity among field clones was 68% reflecting expected genetic variation. Approximately 15% of the field clones were not related. RAPD analysis is a useful tool for genetic relationship studies in lowbush blueberry and may provide similarity information for future pollination/productivity research.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.808
Threshold uncertainty score0.554

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.191
GPT teacher head0.380
Teacher spread0.189 · 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