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Record W2978766048 · doi:10.11425/sst.7.81

Bio-argumentation for purifying VOCs

2018· article· en· W2978766048 on OpenAlexaff
Iwao Tamura, Adachi Kazuyoshi

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicChemistry and Chemical Engineering
Canadian institutionsFuture Earth
Fundersnot available
KeywordsArgumentation theoryComputer scienceBiochemical engineeringRisk analysis (engineering)ChemistryEpistemologyBusinessEngineeringPhilosophy

Abstract

fetched live from OpenAlex

The SepaTech Micro-bubble System has been applied to bio-argumentation by introducing an ERP KB-12 strain into contaminated soil with volatile organic compounds such as benzene. The ERP KB-12 strain can decompose benzene under aerobic conditions and effectively reduce the odor derived from contaminants in a short time. As a result of bio-argumentation at the old site of N company's Kobe factory in Chuo-ku, Kobe, we succeeded in improving the odor by injecting a relatively small amount of the ERP KB-12 strain. The odor before implementing bio-argumentation recorded about 960 on average in some plots, but it was irreversibly reduced to almost 0 about 24 hours after injection. By combining the ERP KB-12 strain and SepaTech Micro-bubble System, it is possible to effectively purify volatile organic compounds such as benzene in situ.

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 categoriesInsufficient payload (model declined to judge)
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 score0.017
Threshold uncertainty score0.984

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 communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0170.000

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 head0.178
GPT teacher head0.527
Teacher spread0.349 · 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.

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

Citations0
Published2018
Admission routes1
Has abstractyes

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