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Record W2792647900 · doi:10.5539/jas.v10n4p335

Penicillium and Talaromyces Communities of Sugarcane Soils (Saccharum officinarum L.): Ecological and Phylogenetic Aspects

2018· article· en· W2792647900 on OpenAlexvenueno aff
Sérgio Murilo Sousa Ramos, Roberta Cruz, Renan do Nascimento Barbosa, Alexandre Reis Machado, Antônio Félix da Costa, Cristina Maria de Souza‐Motta, Neiva Tinti de Oliveira

Bibliographic record

VenueJournal of Agricultural Science · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPlant Pathogens and Fungal Diseases
Canadian institutionsnot available
FundersUniversidade Federal de PernambucoEmpresa Brasileira de Pesquisa Agropecuária
KeywordsPenicilliumBiologyBotanySoil waterEcologySpecies diversity

Abstract

fetched live from OpenAlex

Penicillium and Talaromyces are fungal genera with high ecological and biotechnological importance. However, studies on exploration and ecology of these fungi in soils are scarce. The objectives of this study were to evaluate the species diversity of these genera in soils of sugarcane and fallow. Identification of the isolates was performed by morphological examination and partial sequencing of Beta-tubulin. For ecological analyses, indexes were applied and principal component analysis (PCA) was performed. A total of 1,344 isolates were obtained: 1,108 of Penicillium (13 species) and 236 of Talaromyces (three species). Seven isolates did not cluster with any known species. The diversity and equitability indexes were similarly high for the two areas analyzed. Penicillium wotroi and Talaromyces murroi were more abundant. The PCA was significant and showed 2 groups: fallow and cultivated. Soils of sugarcane cultivation present distinct communities of Penicillium and Talaromyces species that are rare and/or not yet described by science.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.009
GPT teacher head0.225
Teacher spread0.216 · 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 source (direct Gemma or distilled Codex), 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

Citations8
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Agricultural Science→Same topicPlant Pathogens and Fungal Diseases→French-language works237,207→