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Record W2021662966 · doi:10.1139/cjm-2013-0287

Evaluation of<i>Streptomyces</i>spp. for their plant-growth-promotion traits in rice

2013· article· en· W2021662966 on OpenAlexvenueno aff
Subramaniam Gopalakrishnan, Vadlamudi Srinivas, Shravya Apparla, Prakash Bandikinda, Rajendran Vijayabharathi, Ratna Kumari Bhimineni, O P Rupela

Bibliographic record

VenueCanadian Journal of Microbiology · 2013
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant-Microbe Interactions and Immunity
Canadian institutionsnot available
Fundersnot available
KeywordsStreptomycesBiologyStreptomyces griseusBenomylTrichoderma virideHorticultureBotanyAgronomyFood scienceFungicideBacteria

Abstract

fetched live from OpenAlex

Five strains of Streptomyces (CAI-17, CAI-68, CAI-78, KAI-26, and KAI-27) were previously reported to have potential for charcoal rot control and plant growth promotion (PGP) in sorghum. In this study, those 5 Streptomyces strains were characterized for their enzymatic activities and evaluated for their PGP capabilities on rice. All the Streptomyces strains were able to produce lipase and β-1,3-glucanase; grew in NaCl (up to 8%), at pH 5-13, and at temperatures 20-40 °C; and were resistant to ampicillin, sensitive to nalidixic acid, and highly sensitive to chloramphenicol, kanamycin, streptomycin, and tetracycline. They were highly tolerant to the fungicide bavistin but were highly sensitive to benlate, benomyl, and radonil. When evaluated on rice in the field, Streptomyces significantly enhanced tiller and panicle numbers, stover and grain yields, dry matter, root length, volume and dry weight, compared with the control. In the rhizosphere at harvest, microbial biomass carbon and nitrogen, dehydrogenase activity, total nitrogen, available phosphorus, and % organic carbon were also found significantly higher in Streptomyces-treated plots than in the control plots. This study further confirms that the selected Streptomyces have PGP activities.

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 score0.702
Threshold uncertainty score0.997

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 science0.0000.000
Research integrity0.0000.000
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 head0.035
GPT teacher head0.226
Teacher spread0.191 · 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

Citations42
Published2013
Admission routes1
Has abstractyes

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