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The results of study of collection samples of spring soft wheat in the Middle Volga Region

2020· article· en· W3110926271 on OpenAlexaboutno aff
Irina F. Demina

Bibliographic record

VenueAgricultural science Euro-North-East · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicWheat and Barley Genetics and Pathology
Canadian institutionsnot available
Fundersnot available
KeywordsRipeningHorticultureYield (engineering)Grain yieldGeographyBiologySpring (device)AgronomyAnimal sciencePhysics

Abstract

fetched live from OpenAlex

During the research, there were studied 186 varieties of spring soft wheat of different ecological and geographical origin to develop valuable initial material for new varieties in the conditions of Middle Volga. The studies were carried out in accordance with the methodic recommendations of VIR. According to the duration of the growing season, the samples were divided into three groups: early ripening – 29.4 %, mid-ripening – 45.0 % and mid-late-ripening – 25.6 %. The largest number of high-yielding varieties belongs to the mid-ripening group. Five groups are distinguished according to plant height: above 120 cm – tall, 120-105 cm – medium-grown, 104-85 cm – undersized, 84-60 cm – semi-dwarfs, less than 60 cm – dwarfs. A group of semi-dwarfs showed high resistance to lodging. The yield of productive genotypes in this group is 192-210 g/m 2 . The analysis of the elements of the yield structure showed the varieties that exceeded the standard in the number of grains in the ear (32.8 pcs.): Annet (38.2 pcs.), Baganskaya 95 (36.5 pcs.), Riks (37.7 pcs.), Lubninka (36.8 pcs.), (Russia, West Siberian Region), Biryusa (37.2 pcs.) (Russia, East Siberian Region), by grain weight per ear – Russian varieties from East Siberian, West Siberian and Lower Volga Regions (0.96-1.52 g), and varieties of foreign selection from North America (0.89-1.64 г). According to the yield the following varieties significantly exceeded the standard variety Kinelskaya Niva (310 g/m 2 ) by 30-54 g/m 2 (LSD 05 = 22.5 g/m 2 ): Russian varieties Annet, Baganskaya 95, Lavrusha, Tarskaya 10, Pamyati Maistrenko, Omskaya 39, Duet (West Siberian region), Voevoda (Lower Volga region) and Uyarochka (East Siberian region), foreign varieties Aktyube 10 (Kazakhstan) and Granit (Canada). The varieties with complex resistance to the main types of leaf diseases (leaf rust and powdery mildew) have been identified: Norwell, Granit, Dandy, CDC Merlin (Kanada), Lavrusha, Tarskaya 10 (West Siberian Region), Tybalt (Netherlands), Voevoda (Lower Volga Region), Etyud (Ukraine). The identified varieties were used as parental forms in crosses.

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 score0.589
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.070
GPT teacher head0.218
Teacher spread0.148 · 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

Citations5
Published2020
Admission routes1
Has abstractyes

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