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ASSESSMENT OF SOYBEAN SOURCE MATERIAL (BASE LINE) IN RESPECT OF PRODUCTIVITY AND RESISTANCE TO FUNGAL PATHOGENS IN THE CLIMATE OF THE PRIMORSKY KRAI

2019· article· en· W4407554033 on OpenAlexaboutno aff
E.A. Vasina, О. И. Хасбиуллина

Bibliographic record

VenueFar Eastern Agrarian Herald · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoybean genetics and cultivation
Canadian institutionsnot available
Fundersnot available
KeywordsProductivityResistance (ecology)Crop productivityBiotechnologyBiologyEnvironmental scienceAgronomyCropEconomicsEconomic growth

Abstract

fetched live from OpenAlex

The article presents the findings of the investigations carried out on the soybean source material that resulted in identification of the varieties of soybean selected by certain quantitative characteristics and ability to resist fungal pathogens in the climate of the Primorsky Krai. The varieties were taken from the collection nursery. It was found that some varieties of different ecological and geographical origin fully realize their genetic potential of productivity under climatic conditions of the region. The maximum productivity was found in the variety of Chinese breeding HEI-he 4 - 7.5 g. Among the tested varieties, the most resistant to Septoria spot (septoriosis) was a sample of Canadian breeding - 0319, degree of pathogen damage amounted to 32.2%, which is 12.8% less than the standard. According to the complex features and immunological characteristics the following varieties can be singled out: Cordoba (Austria) and Cmbura 1 (Belarus) from the European group which are resistant to false mildew.

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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.018
GPT teacher head0.232
Teacher spread0.214 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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