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Record W2907047526 · doi:10.5539/jas.v11n2p151

Growth and Tolerance of Pleurotus ostreatus at Different Selenium Forms

2019· article· en· W2907047526 on OpenAlexvenueno aff
Marliane de Cássia Soares da Silva, José Maria Rodrigues da Luz, Ana Paula S. Paiva, Daniele Ruela Mendes, Alexandrina A. C. Carvalho, Juliana Naozuka, Maria Catarina Megumi Kasuya

Bibliographic record

VenueJournal of Agricultural Science · 2019
Typearticle
Languageen
FieldNursing
TopicSelenium in Biological Systems
Canadian institutionsnot available
Fundersnot available
KeywordsPleurotus ostreatusSeleniumMyceliumSodium selenateChemistryFood scienceSelenateSodiumBiofortificationMycotoxinBotanyBiologyMushroomMicronutrientOrganic chemistry

Abstract

fetched live from OpenAlex

Selenium is an important element in physiological and metabolic processes. Due to low Se concentration in most of the soils, strategies as enrichment and biofortification have been used to increase its incorporation in food. The fungus has capacity to absorb, accumulate and transform Se inorganic into organic compounds. However, the concentration and chemical forms of Se used for enrichment can affect the mycelial growth and mushrooms production. Thus, the aim of this study was to analyze the capacity of Pleurotus ostreatus in absorb, accumulate and tolerate growing concentrations of different Se chemical forms. In the disc of agar with mycelium was added 20 mL of PDA medium and Se concentration (0-200 mg L-1) in the forms of sodium selenite, sodium selenate or selenomethionine (SeMet). The greatest inhibition of mycelial growth and biomass production were observed in highest Se concentration. Regardless of the Se level, SeMet and sodium selenite were more harmful to the P. ostreatus growth than sodium selenate. However, the highest Se accumulation in the mycelium was observed in culture medium with sodium selenite. Thus, Se supplementation in the forms of sodium selenite was more indicated to enrichment of P. ostreatus mushrooms than sodium selenate and SeMet.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.525
Threshold uncertainty score0.236

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 head0.010
GPT teacher head0.225
Teacher spread0.215 · 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

Citations11
Published2019
Admission routes1
Has abstractyes

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