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Record W2527065626 · doi:10.21769/bioprotoc.1891

EST-SSR Analysis and Cross-species Transferability Study in Lavandula

2016· article· en· W2527065626 on OpenAlexafffund
Ayelign M. Adal, Zerihun A. Demissie, Soheil S. Mahmoud

Bibliographic record

VenueBIO-PROTOCOL · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPlant biochemistry and biosynthesis
Canadian institutionsNational Research Council CanadaUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
FundersAgriculture and Agri-Food CanadaNatural Sciences and Engineering Research Council of CanadaGenome British Columbia
KeywordsTransferabilityLavandulaBiologyComputer scienceLavenderBotanyMachine learning

Abstract

fetched live from OpenAlex

The genus Lavandula comprises of several economically important lavender species that are mainly cultivated worldwide for essential oil production. Identification of lavender species and their cultivars has been a huge bottleneck in lavender industries due to lack of appropriate identification mechanisms. Recent advances in modern technologies would help to address these identification issues through development of potential molecular markers, including simple sequence repeats (SSRs). SSRs can be developed from specific species, and can be potentially used for related species, which lack the source sequences to develop species-specific SSRs. Here, we describe the guidelines and steps of identifying and analyzing SSRs from expressed sequence tag (EST) sequences of lavender species. We also detail the validation procedures of selected EST-SSRs in distinguishing source (donor) species as well as related 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.

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)0.001
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: none
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.018
GPT teacher head0.311
Teacher spread0.293 · 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

Citations1
Published2016
Admission routes2
Has abstractyes

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