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Record W4206060590 · doi:10.1139/cjm-2021-0337

Diversity of soil fungi in the vineyards of Changli region in China

2022· article· en· W4206060590 on OpenAlexvenueno aff
Hejing Yan, Chao Ge, Jiefang Zhou, Jun Li

Bibliographic record

VenueCanadian Journal of Microbiology · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFermentation and Sensory Analysis
Canadian institutionsnot available
FundersNanjing Agricultural UniversityNatural Science Foundation of Hebei ProvinceNational Natural Science Foundation of China
KeywordsVineyardAscomycotaAbundance (ecology)BasidiomycotaWineBotanyFungal DiversityBiologyEcologyHorticulture

Abstract

fetched live from OpenAlex

This study aimed to elucidate the fungal diversity of Changli vineyard soil in China. High-throughput sequencing technology was used to investigate the diversity and composition of soil fungi in five vineyards from different geographical locations in Changli. Although the five vineyards had similar fungal communities, the diversity, composition, and distribution of the high-abundance species differed. Ascomycota, Basidiomycota, and Mortierellomycota were dominant phyla. Among the 14 high-abundance genera of fungi, Odiodendron, Pleotrichocladium, and Plectosephalella have rarely been reported in other vineyards and are unique to the Changli region. In addition, Solicoccozyma aeria and Solicoccozyma terrea were the dominant species in the five vineyards and have rarely been reported in domestic vineyards. Additionally, Rhizophagus, Wardomyces, Mortierella, Volutella, and Cryptococcus were significantly different among the five vineyard soils. Among these species, Mortierella was highly abundant in each vineyard, but its contents were significantly different across vineyards. These findings enrich the information on the composition and diversity of soil fungi in the vineyard of the Changli region, which helps to explore the regional or distinctive sensorial attributes of wine from the perspective of microbial biogeography.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.392
Threshold uncertainty score0.988

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 head0.021
GPT teacher head0.188
Teacher spread0.168 · 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 designObservational
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

Citations17
Published2022
Admission routes1
Has abstractyes

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