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Record W3088439882 · doi:10.5539/jps.v10n1p40

Identification and Characterization of Bacterial Agents Causing Moderate Damage and Souring of the Fig Fruits

2021· preprint· en· W3088439882 on OpenAlexvenueno aff
Seyedeh Asiyeh Mousavi, Nader Hasanzadeh

Bibliographic record

VenueJournal of Plant Studies · 2021
Typepreprint
Languageen
FieldAgricultural and Biological Sciences
TopicPhytochemistry and biological activities of Ficus species
Canadian institutionsnot available
Fundersnot available
KeywordsIdentification (biology)Characterization (materials science)EngineeringBiologyMaterials scienceBotanyNanotechnology

Abstract

fetched live from OpenAlex

In order to determine the factors of decay and sourness of fig fruits, in the summer of 2016-17, 60 leaf, fruit and stem samples from different regions of Tehran, Varamin (Qal'e No), Mazandaran (Amol, Noor and Sari), Lorestan from Iran and a branch of fig fruit sample from Italy were collected. We obtained 30 isolates from the sample. The pathogenicity of 30 isolates were confirmed by artificially inoculation using fig fruits. They were also characterized based on key phenotypic traits. All 30 isolates showed hypersensitivity reaction to tobacco, pelargonium and did not show pathogenicity to potato tubers. 16S rRNA gene of the 10 representative isolates were sequenced. Ten isolates were identified as Stenotrophomonas maltophilia, Pseudomonas aeruginosa, Pseudomonas fulva, Brevibacterium linens, Pseudomonas fragi, Bacillus licheniformis, Bacillus paralicheniformis and Bacillus cereus based on the determined sequences. None of the isolates caused fruit rot but typical disease symptoms were observed on fig leaves and fruits. This is the first report of the presence of pathogenic bacteria on fig trees in Iran.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.476
Threshold uncertainty score0.147

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 head0.056
GPT teacher head0.247
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

Citations0
Published2021
Admission routes1
Has abstractyes

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