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Record W2782783030 · doi:10.2166/wst.2018.023

Evaluation of Ficus benjamina wood chip-based fungal biofiltration for the treatment of Tequila vinasses

2018· article· en· W2782783030 on OpenAlexaff
Ramírez Camperos Esperanza, Gerardo Buelna, Díaz-Godínez Gerardo, Estrada-Arriaga Edson Baltazar

Bibliographic record

VenueWater Science & Technology · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicEnzyme-mediated dye degradation
Canadian institutionsCentre de Recherche Industrielle du Québec
Fundersnot available
KeywordsBiofilterChemistryTrametes versicolorBagasseChemical oxygen demandPhanerochaetePulp and paper industryBiochemical oxygen demandBiodegradationChrysosporiumLaccaseEnvironmental engineeringEnvironmental scienceWastewaterOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract This study was focused on the application of an aerobic biofiltration (BF) with Ficus benjamina wood chips as support medium, inoculated with two basidiomycete fungi, Phanerochaete chrysosporium (BF 1) and Trametes versicolor (BF 2), to treat Tequila vinasses from a Tequila industry. The biofiltration system was compared with a biofilter system without basidiomycete fungi (BF W), in order to determine the influence of fungi on the treatment of vinasses. Three different vinasses/water ratios (30/70, 40/60, and 50/50) were evaluated. The maximum removals of chemical oxygen demand (COD) obtained during each operation step were 72% (BF 1), 72% (BF 2), and 8% (BF W) for 30 vinasses/70 water; 72% (BF 1), 73% (BF 2), and 66% (BF W) for 40 vinasses/60 water; and 22% (BF 1), 20% (BF 2), and 18% (BF W) for 50 vinasses/50 water. The total organic carbon (TOC) removal was significantly increased using a volumetric organic load of 5.5 kg COD m−3d−1. During the operation of the biofilters, the enzymatic activity of laccase was present, even at the step of highest concentration of vinasses.

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)0.001
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 score0.128
Threshold uncertainty score0.378

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.040
GPT teacher head0.279
Teacher spread0.239 · 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
Published2018
Admission routes1
Has abstractyes

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