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Record W4377091165 · doi:10.3390/agriculture13051089

The Recent Use of Plant-Growth-Promoting Bacteria to Promote the Growth of Agricultural Food Crops

2023· article· en· W4377091165 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAgriculture · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant-Microbe Interactions and Immunity
Canadian institutionsUniversity of WaterlooBayer (Canada)
Fundersnot available
KeywordsAgriculturePlant growthBiotechnologyBiologyBacteriaAgronomyEcology

Abstract

fetched live from OpenAlex

In the past 15–20 years, the employment of Plant-Growth-Promoting Bacteria (PGPB) to facilitate the growth of agricultural food crops has increased dramatically. These beneficial soil bacteria, whose use and demonstrations of efficacy have previously been largely limited to the laboratory, have now been shown to be effective under field conditions. In addition, the mechanisms that these bacteria utilize to facilitate plant growth are now mostly well characterized. Moreover, several companies across the globe have commercialized a number of PGPB and there is every indication that this trend will continue to grow. As a consequence of these developments, in this review article, a large number of recent reports on the successful testing of many different types of PGPB and their effects on various food crops is discussed.

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.

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 score0.772
Threshold uncertainty score0.438

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.026
GPT teacher head0.209
Teacher spread0.183 · 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