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Study of samples of spring barley from the collection of the All-Russian institute of crop production for resistance to biotic stress

2021· article· en· W3139289687 on OpenAlexaboutno aff
Н. А. Сурин, А. Г. Липшин, Н С Козулина, С. А. Герасимов, А В Василенко

Bibliographic record

VenueIOP Conference Series Earth and Environmental Science · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural Productivity and Crop Improvement
Canadian institutionsnot available
Fundersnot available
KeywordsBipolarisCropBiologyAgronomyHordeum vulgareFusariumFodderResistance (ecology)HorticulturePoaceae

Abstract

fetched live from OpenAlex

Abstract Spring barley in Eastern Siberia occupies an important place among the main grain crops and is grown mainly for fodder purposes. Its grain can also be used in bakery, confectionery, pharmaceutical and other industries. In addition, barley is the main raw material for brewing. One of the reasons that have a significant impact on the formation of the yield and technological qualities of barley is the high susceptibility of the crop to phytopathogenic microflora, the strong development of which leads to their decrease. Barley is the most affected crop of the cereal group. The most common diseases are root rot, represented mainly by pathogens of the fusarium - helminthosporium complex. Fungi of the genus Bipolaris were identified in the forest-steppe zone, in the subtaiga zone - species of the genus Fusarium. Both are capable of infecting almost all underground and aboveground plant organs. According to the long-term data, root rot annually reduces grain yield by 20 - 23%. Samples of barley with resistance to damage by dark brown and striped barley spotting (7 points, reaction type - R) were identified; Heritage (USA), Condor, AC Albright (Canada), Malva (Latvia), Wash, Symphony (Ukraine), Viner (Kirov region), Pervocelinnik (Orenburg region), Vorsinsky 2 (Altai region) and Zauralsky 1 (Tyumen region).

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 score0.475
Threshold uncertainty score0.767

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 studies0.0000.001
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 head0.028
GPT teacher head0.203
Teacher spread0.175 · 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

Citations2
Published2021
Admission routes1
Has abstractyes

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Same venueIOP Conference Series Earth and Environmental ScienceSame topicAgricultural Productivity and Crop ImprovementFrench-language works237,207