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Record W1935106589 · doi:10.4141/cjps-2015-084

N<sub>2</sub> fixation ability of different dry bean genotypes

2015· article· en· W1935106589 on OpenAlexafffundvenueabout
Mehdi Farid, Alireza Navabi

Bibliographic record

VenueCanadian Journal of Plant Science · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicLegume Nitrogen Fixing Symbiosis
Canadian institutionsAgriculture and Agri-Food CanadaUniversity of Guelph
FundersAgriculture and Agri-Food CanadaAgricultural Adaptation Council
KeywordsNitrogen fixationBiologyAgronomyGenotypeHorticultureGene

Abstract

fetched live from OpenAlex

Farid, M. and Navabi, A. 2015. N 2 fixation ability of different dry bean genotypes. Can. J. Plant Sci. 95: 1243–1257. Common bean (Phaseolous vulgaris L.) is generally known as a weak N 2 fixer compared with other legumes. The availability of genetic variation for N 2 fixation potential of common bean supports the idea that symbiotic nitrogen fixation (SNF) in common bean can be improved through breeding. To assess the potential N 2 fixation of selected common bean genotypes, 12 bean varieties including three Andean and nine Middle American were screened for SNF ability and related traits in controlled environments and field trials in Ontario, Canada. A non-nodulating mutant, R99, was used as the reference plant to estimate nitrogen derived from the atmosphere (Ndfa) through the natural 15 N abundance method. Significant variation was found among the bean genotypes for Ndfa and its related traits. Environmental and genotype by environmental effects significantly influenced Ndfa and its related traits. The three Andean bean genotypes were superior to Middle American genotypes for nodulation ability, while the Middle American genotypes were generally stronger nitrogen fixers in optimum soil moisture conditions. In general, nitrogen fixation was found to be significantly associated with seed yield and carbon isotope discrimination, an indicator of water use efficiency.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.486
Threshold uncertainty score0.987

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 science0.0010.000
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 head0.020
GPT teacher head0.198
Teacher spread0.178 · 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

Citations44
Published2015
Admission routes4
Has abstractyes

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