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Interactive Effect of Silicate Solubilizing Bacterium Pseudomonas baetica and Fly Ash Application on Wheat Crop

2024· article· en· W4406247450 on OpenAlexaff
Gourav Chopra, Amit Kumar Sharma, Meena Rani, Leela Wati

Bibliographic record

VenueAgricultural Research Journal · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSilicon Effects in Agriculture
Canadian institutionsGovernment of Manitoba
Fundersnot available
KeywordsCropPseudomonasBacteriaBiologyAgronomyHorticultureBotanyGenetics

Abstract

fetched live from OpenAlex

Fly ash, a byproduct of thermal power plants, is a rich source of essential nutrients, including silicon, which plays a key role in plant growth, stress tolerance, and overall physiology. Silicon is typically found in fly ash in the form of water-insoluble silicates, which can be made bioavailable to plants through the action of silicate-solubilizing bacteria. This study investigates the synergistic effects of silicate-solubilizing bacterial inoculation and fly ash amendment on wheat (Triticum aestivum) growth under controlled pot-house conditions. The results demonstrate that soil amendment with up to 3% fly ash, in conjunction with bacterial inoculation, supported the survival of the bacteria and significantly enhanced wheat growth and yield. These findings suggest that the combination of fly ash and silicatesolubilizing bacteria could offer a sustainable approach to improving crop productivity.

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)0.002
metaresearch head score (Gemma)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.392
Threshold uncertainty score0.599

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.023
GPT teacher head0.326
Teacher spread0.304 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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