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Record W2011411285 · doi:10.5513/jcea01/12.3.941

CULTIVAR IMPACT ON THE CHEMICAL CONTENT AND GRAIN TECHNOLOGICAL QUALITIES OF SOME DURUM WHEAT CULTIVARS

2011· article· en· W2011411285 on OpenAlexaboutno aff
T. Kolev, Nurettin Tahsin, Lyubka Koleva, Kalin Ivanov, H. Dzhugalov, Maria Mangova, G. Delchev

Bibliographic record

VenueJournal of Central European Agriculture · 2011
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicCrop Yield and Soil Fertility
Canadian institutionsnot available
Fundersnot available
KeywordsCultivarAgronomyGlutenYield (engineering)Grain yieldWheat grainBiologyMathematicsFood scienceMaterials science

Abstract

fetched live from OpenAlex

A comparative experiment was carried out with five Bulgarian and five foreign durum wheat cultivars. The aim of the experiment was to determine the chemical content and grain technological quality of some Bulgarian and foreign durum wheat cultivars grown under the agriecological conditions of Southern Bulgaria. The Vazhod cultivar proved to give the highest durum wheat grain yield, followed by Beloslava cultivar. Out of the foreign durum wheat cultivars the Durumko was notable for its higher productivity. The crude protein content in the grain was highest in Zagorka, Yavor and Yukon. The highest yield of gluten was reported in Beloslava, Vazhod and Zagorka.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.005
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.052
GPT teacher head0.218
Teacher spread0.166 · 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 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

Citations4
Published2011
Admission routes1
Has abstractyes

Explore more

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