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Record W2002873915 · doi:10.1080/08927014.2013.825901

<i>Pleurotus ostreatus</i>biofilms exhibit higher tolerance to toxicants than free-floating counterparts

2013· article· en· W2002873915 on OpenAlexaboutno aff
Lorena Pesciaroli, Maurizio Petruccioli, Federico Federici, Alessandro D’Annibale

Bibliographic record

VenueBiofouling · 2013
Typearticle
Languageen
FieldMedicine
TopicFungal Biology and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsBiofilmPleurotus ostreatusChemistryMicrobiologyExtracellular polymeric substanceChemical oxygen demandWastewaterFood scienceBiologyEnvironmental engineeringBacteriaEnvironmental science

Abstract

fetched live from OpenAlex

The MBEC(TM)-High Throughput Assay based on the Calgary Biofilm Device was used to produce and to characterize Pleurotus ostreatus biofilms. Hydroxyapatite coating of pegs was required to enable biofilm attachment; biofilm amounts and homogeneity of distribution were markedly improved upon removal of non-sessile biomass after 48 h from inoculation. Scanning electron microscopy showed surface-associated and multi-layered growth stabilized by the presence of an extracellular matrix (ECM). Biofilms had higher contents of total sugars and ECM than their free-floating counterparts. Tolerance to Cr(VI) in the former was about twice that of the latter as inferred by the respective inhibitory concentrations (48.4 vs 24.1 mM and 114.5 vs 61.0 mM in 4- and 7-d-old cultures, respectively). Biofilms also displayed superior olive-mill wastewater (OMW) treatment efficiency along 5 consecutive batches leading to chemical oxygen demand and total phenol removals higher than 50 and 90%, respectively. Laccase activity peaks in biofilm cultures grown on OMW were significantly higher than those in free-floating cultures.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.017
GPT teacher head0.261
Teacher spread0.244 · 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 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

Citations5
Published2013
Admission routes1
Has abstractyes

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