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Winter wheat of the VIR collection in the Middle Cis-Ural region

2022· article· en· W4224255429 on OpenAlexaboutno aff
Irina Torbina

Bibliographic record

VenueIOP Conference Series Earth and Environmental Science · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural Productivity and Crop Improvement
Canadian institutionsnot available
Fundersnot available
KeywordsCropGeographyMathematicsHorticultureBiologyForestry

Abstract

fetched live from OpenAlex

Abstract The aim of the study was to identify the sources of economically useful traits of winter wheat from the collection of the Federal State Budgetary Scientific Institution “Federal Research Center All-Russian Institute of Plant Genetic Resources N.I. Vavilov ”(VIR) for adaptive breeding of this crop in the Middle Cis-Ural region. Highly productive varieties Harvard (k-66051 USA), Rasad (k-66087 Kazakhstan), Augusta (k-63929 Rostov region), Dominanta (k-64620 Rostov region) and AC Buteo (k-66054 Canada) were the most adaptive according to the method of L.A. Zhivotkova, Z.A. Morozova, L.I. Sekatueva (1994). But they were characterized by significant yield variability (V=36-130 %). Varieties Nastya (k-65675 Stavropol), WA007970 (k-66043 USA), AC Buteo, PA8769-158 (k-65943 USA), Ransom (k-65236 USA) and Farnum (k-65944 USA) showed high homeostaticity (Hom = 1.8-24.7). These varieties were the most productive and stable also according to the method of E.D. Nettevich, A.I. Morgunova, M.I. Maksimenko (1985). Varieties AC Buteo (6), WA007970 (15), PA8769-158 (16), Farnum (21), Ransom (21), Nastya (22) were distinguished by the sum of ranks; the sum of the ranks of the standard Moskovskaya 39 was 22. Sources of increased grain size (weight of 1000 grains 41.2-54.0 g) were identified - varieties Nemchinovskaya 17 (k-65756), Harvard, Rasad, Nureke (k-66088), Alija (k-66089), Avesta (k- 64491), Agra (k-64492), Shestopalivka (k-65060); grain content of a ear (30.9-47.3 pcs.) - Dzhangal (k-65610), Slavitsa (k-65656), Nastya, Alija. High grain glassiness of 90-100% for two years of study (2019-2020) was obtained in varieties Nastya, Farnum, WA007970, AC Buteo and Ransom.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.901
Threshold uncertainty score0.682

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 studies0.0010.001
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 head0.022
GPT teacher head0.171
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 designBench or experimental
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
Published2022
Admission routes1
Has abstractyes

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