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Record W2379557859

Identification Seed Purity of Six Maize Hybrids by SSR Markers

2010· article· en· W2379557859 on OpenAlexvenueno aff
Rixin Wang

Bibliographic record

VenueSeed · 2010
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture, Soil, Plant Science
Canadian institutionsnot available
Fundersnot available
KeywordsHybridSowingBiologyHorticultureAgronomy
DOInot available

Abstract

fetched live from OpenAlex

Purity of hybrids seed is one of the key factor influencing maize industry.To establish rapid and accurate purity detection technique is an effective measure for maize seed purity controlling.Six primers were selected from 30 pairs of SSR,which gave stable and polymorphic amplification profiles.The amplification bands of hybrids were complementary with that of the parents.Umc 1002,bnlg 1112,bnlg 238,Umc 1153,phi 072 and bnlg 240 could detect the purity of Nongda No.108,Xundan No.20,Zhengdan No.958,Lainong No.14,Lainong No.15 and Luyu No.16,respectively.Alleles frequency of the complement bands were relatively low with a range of 0.028-0.072,which ensured to separate self-cross accurately and off-type individuals with hybrid plants.The results obtained by SSR markers were a little less than that of field plot planting testing,but there were no significant difference when statistical analysis was performed.The correlation coefficient was 0.8434.The SSR identification system established in this paper could be used to identify seed purity of six maize hybrid varieties rapidly.

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 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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.839
Threshold uncertainty score0.182

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 head0.006
GPT teacher head0.191
Teacher spread0.185 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations0
Published2010
Admission routes1
Has abstractyes

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