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COMPARATIVE ANALYSIS OF BARLEY SPECIES OF THE WORLD COLLECTION OF THE RESEARCH INSTITUTE OF PLANT INDUSTRY IN THE CONDITIONS OF EASTERN SIBERIA

2017· article· en· W2777315740 on OpenAlexaboutno aff
Сергей Герасимов, С. А. Герасимов

Bibliographic record

VenueBulletin of Kemerovo State University Series Biological Engineering and Earth Sciences · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural Productivity and Crop Improvement
Canadian institutionsnot available
Fundersnot available
KeywordsCultivarBiologyProductivityYield (engineering)HorticultureAgronomyBotany

Abstract

fetched live from OpenAlex

The current paper presents the results of a comparative evaluation of 74 samples of the world collection preserved at the Vavilov Research Institute of Plant Industry named in 2013-2016. The analyses features individual elements of productivity, yield and the variation degree (Cv, %). The samples of the six-rowed barley of Leningradskiy and Potra cultivars have been found early-ripening (vegetation period 68-69 days). The highest capacity for productive tillering (1.93-2.40 productive stems) have been registered in the cultivars of Rupal, Corona, Malva, Chelyabinets 2, Nutans 302, Belgorodets and Talovsky 9. The best graininess of the ear belongs to the following cultivars: 18/7, Tduneja, Spratt, Sv. 66905, Milns Golden Promise, Vladimir. According to the mass of 1000 grains (40.0-52.3 g), T-12 (Nutans 129), Yastreb, Belgorodets, Talovsky 9, Yak-401, Nudum 95, Codac, Vakula, Potra, Kolchan. The best grain mass from one plant (1.40-1.79 g) was registered in 18/7, Tduneja, T-12 (Nutans 129), Elf, Belgorodets, Talovsky 9, Heimdal, Nudum 95, Codac, Vakula and Kolchan. According to the productivity and resistance to lodging, the following samples proved to be stable during the research period: M 1913/88 (Czechoslovakia), Asem (Kazakhstan), Etienne (Canada). Their yield reached 555.0-575.0 g/m2, which is 8.2-12.1 % higher than that of the standard Acha cultivar. The barley samples studied in the research are of practical interest for solving regional breeding problems in Eastern Siberia.

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 score0.690
Threshold uncertainty score0.772

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 studies0.0000.002
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 head0.064
GPT teacher head0.239
Teacher spread0.175 · 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

Citations1
Published2017
Admission routes1
Has abstractyes

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