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Record W2374292415 · doi:10.5539/jas.v8n6p109

Comparative Productivity and Adaptive ability of Forage Pea (Pisum sativum L.) and Vetch (Vicia sativa L.) Cultivars

2016· article· en· W2374292415 on OpenAlexvenueno aff
Natalia Georgieva, Valentin Kosev

Bibliographic record

VenueJournal of Agricultural Science · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicGenetic and Environmental Crop Studies
Canadian institutionsnot available
Fundersnot available
KeywordsSativumVicia sativaPisumBiologyForageAgronomyCultivarYield (engineering)ProductivityVicia fabaMathematicsHorticulture

Abstract

fetched live from OpenAlex

Adaptive potential of forage pea and vetch accessions was estimated based on seed yield and yield components. А varietal-specific reaction of the studied accessions to environmental conditions was established. The conducted assessment of selective value of genotypes gave accurate data for breeding of highly productive forms. Among studied pea varieties the following were characterized to possess adaptive ability and selective value - Glyans for numbers of seeds (1000 seed weight and seed yield), Kamerton for plant height, and Svit for seed yield. In terms of general adaptive ability and stability in plant height with the greatest value as a starting material was vetch variety Vilena. Moldovskaya was of interest in terms of 1000 seed weight. Liya is preferred vetch genotype for improving the number of seeds per plant and for selection of new forms for improving grain yield.

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 score0.004

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 head0.024
GPT teacher head0.232
Teacher spread0.207 · 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

Citations1
Published2016
Admission routes1
Has abstractyes

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