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Record W1977248415 · doi:10.4236/aim.2014.412082

Comparison of the Bacterial Microbiota in a Bale of Collected Cardboard Determined by 454 Pyrosequencing and Clone Library

2014· article· en· W1977248415 on OpenAlexafffund
V. Lalande, Simon Barnabé, Jean‐Charles Côté

Bibliographic record

VenueAdvances in Microbiology · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicMicrobial Community Ecology and Physiology
Canadian institutionsUniversité du Québec à Trois-RivièresAgriculture and Agri-Food Canada
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPyrosequencingBiologyLibrarycardboardFirmicutesProteobacteriaFood science16S ribosomal RNAMicrobiologyBiotechnologyBacteriaGeneticsGene

Abstract

fetched live from OpenAlex

Biofouling, the accumulation of microorganisms, is a major problem in paper mills processing paper and cardboard. This leads to the production of lower quality recycled products. Several studies have focused on the microbial content in the paper mill and the final products. Our aim was to determine the microbial biota in a bale of collected cardboard prior to entering the paper mill. Total genomic DNA was isolated and analyzed using two different methods for comparison purposes: 454 pyrosequencing and clone library. A total of 3268 V6-V8 454 pyrosequencing reads and 322 cloned V6-V8 16S rRNA nucleotide sequences were obtained. Both methods showed the presence of three major bacterial genera: Bacillus, Solibacillus and Paenibacillus, all members of the spore-forming phylum Firmicutes. Pyrosequencing, however, revealed a richer and more diverse bacterial community than clone library. It showed the presence of additional minor Firmicute genera and of a small number of Proteobacteria. The sorting at the recycling plant, the storing, and the processing at the paper mill, the end uses, will all contribute to the bacterial microbiota present in a bale of collected cardboard as revealed here.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.080
Threshold uncertainty score0.458

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 studies0.0000.001
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 head0.005
GPT teacher head0.232
Teacher spread0.227 · 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

Citations2
Published2014
Admission routes2
Has abstractyes

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