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Record W4386552761 · doi:10.51419/202134422.

Evaluation of winter hardiness and drought resistance of alfalfa varieties in the conditions of Northern Kazakhstan

2023· article· en· W4386552761 on OpenAlexaboutno aff
Victor Ostrovsky, Н. И. Филиппова, С. И. Коконов, Tatyana Ryabova, Olga Esenkulova

Bibliographic record

VenueАгроЭкоИнфо · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural Productivity and Crop Improvement
Canadian institutionsnot available
Fundersnot available
KeywordsHardiness (plants)Selection (genetic algorithm)Resistance (ecology)KazakhGeographyAgronomyBiologyForagePastureCultivar

Abstract

fetched live from OpenAlex

Research on the study of the adaptive properties of varieties of alfalfa variable for introduction into the forage production system in the sharply continental conditions of Northern Kazakhstan is an urgent task. The aim of the research is a comparative assessment of alfalfa varieties of different ecological and geographical origin in the conditions of the region. Experimental work was carried out on the basis of stationary field experiments, laid down in the Research and Production Center for Grain Farming named after. A.I. Baraev. The scheme of the experiment, which included 32 varieties of different ecological and geographical origin, including 10 varieties of Kazakhstan selection, 18 varieties of Russian selection, 3 varieties of Canadian selection and 1 variety - Ukrainian. Varieties Raikhan, Shortandinskaya 2, Karaganda 1 Karabalykskaya rainbow, Karabalykskaya Zhemchuzhina, Lyutsiya 14, Lazurnaya of Kazakh selection, varieties Guzel, Muslima, Sarga, Bibinur, Tatar pasture, Uralochka, Nakhodka of Russian selection and varieties Ferax, Rangelander of Canadian selection had an average variability of the trait, about as evidenced by the coefficient of variation V ≤ 20%. .6% drought tolerance. Keywords: ALUCERN VARIABLE, VARIETIES, WINTER HARDER, DROUGH RESISTANCE, VARIATION

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)0.001
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 score0.918
Threshold uncertainty score0.265

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.039
GPT teacher head0.255
Teacher spread0.216 · 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
Published2023
Admission routes1
Has abstractyes

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