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Record W2002617991 · doi:10.4141/cjps08116

Effects of plant-growth-promoting bacteria on growth and yield of pepper under limited nitrogen supply

2009· article· en· W2002617991 on OpenAlexvenueno aff
Francisco M. del Amor, I. Porras

Bibliographic record

VenueCanadian Journal of Plant Science · 2009
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicShallot Cultivation and Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsInoculationPepperSucroseAzospirillum brasilenseChlorophyllBiologyHorticultureFructoseBacteriaPhotosynthesisDry weightSugarRhizobacteriaAgronomyBotanyMicrobial inoculantFood science

Abstract

fetched live from OpenAlex

The influence of plant-growth-promoting bacteria (Azospirillum brasilense and Pantoea dispersa) on sweet pepper plants (Capsicum annuum L.) under limited N supply was studied. Inoculation did not affect leaf CO2 assimilation, Ci/Ca (the ratio of the intercellular to the ambient CO2 concentration), concentration of chlorophylls, chlorophyll fluorescence (maximum quantum efficiency of PSII) or SPAD readings. Total plant dry weight was significantly reduced in both inoculated and non-inoculated plants when the N supply was reduced from 12 (control) to 7 mM, whilst the NO3− and total-N concentrations in the leaves were not significantly affected by inoculation. Inoculation did not affect marketable fruit yield or the pigments (chlorophylls, lycopene and β-carotene) and carbohydrate (sucrose, glucose and fructose) contents in the fruits but flavonoids and anthocyanins were increased significantly by the addition of the bacteria, relative to non-inoculated plants under limited N supply. Key words: Plant-growth-promoting bacteria, Capsicum annuum L., soilless, photosynthesis, chlorophylls, flavonoids

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.015
GPT teacher head0.190
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 source (direct Gemma or distilled Codex), 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

Citations11
Published2009
Admission routes1
Has abstractyes

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