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Record W2332834157 · doi:10.2166/wst.2015.507

Tylosin effect on methanogenesis in an anaerobic biomass from swine wastewater treatment

2015· article· en· W2332834157 on OpenAlexaff
Liliana García‐Sánchez, Marco A. Garzón‐Zúñiga, Gerardo Buelna, Edson Baltazar Estrada‐Arriaga

Bibliographic record

VenueWater Science & Technology · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicPharmaceutical and Antibiotic Environmental Impacts
Canadian institutionsCentre de Recherche Industrielle du Québec
Fundersnot available
KeywordsTylosinMethanogenesisBiomass (ecology)Anaerobic digestionMethaneChemistryAcetogenesisMicroorganismWastewaterMicrobiologyEnvironmental chemistryAntibioticsFood scienceBacteriaBiologyEnvironmental engineeringBiochemistryEcologyEnvironmental scienceOrganic chemistry

Abstract

fetched live from OpenAlex

The effect of different concentrations of tylosin on methane production was investigated: first methanogenesis in a biomass without contact with the antibiotic, and later the ability of the sludge to adapt to increasing concentrations of tylosin. Results showed that, for biomass that had no contact with the antibiotic, the presence of tylosin inhibits the generation of methane even at concentrations as small as 0.01 mg L(-1), and samples at concentrations above 0.5 mg L(-1) produced practically no methane, whereas, in the digesters acclimated in the presence of tylosin at a concentration of 0.01 to 0.065 mg L(-1), methanogenesis is not inhibited in the presence of antibiotic and the generation of methane is improved. This behaviour suggests the microorganisms have developed not only resistance to the antibiotic but also an ability to metabolize it.

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 score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.032
GPT teacher head0.293
Teacher spread0.261 · 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

Citations19
Published2015
Admission routes1
Has abstractyes

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