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Record W2364830977

Current Situation of Wheat Yield and Quality Improvement in Huang-huai Winter Wheat Region

2013· article· en· W2364830977 on OpenAlexaboutno aff
Liu Jia-ping

Bibliographic record

VenueMailei zuowu xuebao · 2013
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture, Soil, Plant Science
Canadian institutionsnot available
Fundersnot available
KeywordsYield (engineering)AgronomyWinter wheatGrain yieldGrain qualityBiologyCommon wheatMaterials science
DOInot available

Abstract

fetched live from OpenAlex

To understand the present situation and problems of wheat breeding in Huang-huai winter wheat region(HHWWR),the yield components and quality parameters of 68 wheat varieties,which passed through the HHWWR yield trial in last ten years,were analyzed.The results indicated that yield and its components of wheat varieties ascended gradually.It was found that the grains per ear played the most important role for yield increase since the standardized coefficients of grains per ear,effective ears/ha and one thousand grain weight was 1.149,1.06 and 0.793,respectively.At the same time,the wheat quality was not improved but deteriorated to some extent.It showed that,through the analysis of the registered and extended varieties,the quality parameters of most varieties reached or exceeded the parameters of the U.S.and Canadian wheat varieties in some years or some sites;however,the quality parameters were not stable in different years and sites.In addition,it also found that the frequency of subunits 5+10 related with good quality was very low while that of the 1BL/1RS translocation was considerably high among the current varieties,lines and parent materials analyzed.As the breeding strategies,much attention should pay to the increase of grains per ear in high yield breeding and subunits 5+10 in quality breeding;moreover,the yield and quality should be improved simultaneously.

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 score0.640
Threshold uncertainty score0.388

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 head0.040
GPT teacher head0.246
Teacher spread0.207 · 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
Published2013
Admission routes1
Has abstractyes

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