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Effects of Inoculants (Chlorobium limicola and Rhodopseudomonas palustris) on Cucumber (Cucumis sativus L.) Seedlings in Stimulating their Nutrient Uptake and Growth

2017· article· en· W2767997086 on OpenAlexvenueno aff
Shao Shuang, Wang Dongdong, Zhu Dan

Bibliographic record

VenueJournal of Nutritional Therapeutics · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Growth Enhancement Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsCucumisRhodopseudomonas palustrisMicrobial inoculantPseudoperonospora cubensisBiologyHorticultureBotanyChemistryBacteriaInoculation

Abstract

fetched live from OpenAlex

Rhizobacteria-containing biofertilizers are perfect tools to promote plant growth for the superiority of reducing environmental damages. Two strains of Chlorobium limicola and Rhodopseudomonas palustris were applied in the experiment as potential inoculants for cucumber seedlings. Significant enhancement in the availability of macronutrient elements N, P and K were observed in soils, and further improvement in their uptake was also obtained in cucumber plants. Accumulation of essential micronutrients Fe and Zn were detected both in the roots and shoots. The two strains increased chlorophyll and carotinoid synthesis, plant height, stem diameter, wet weight and dry weight. Various doses had significant effects on plant growth stimulation, C. Limicola with 107 cells mL-1 and R. Palustris with 108 cells mL-1 seem to be better on the whole.

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 score0.723
Threshold uncertainty score0.292

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 studies0.0000.000
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 head0.037
GPT teacher head0.265
Teacher spread0.228 · 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

Citations1
Published2017
Admission routes1
Has abstractyes

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