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Record W1980624937 · doi:10.1007/s10295-003-0026-4

Biofiltration eliminates nuisance chemical odors from industrial air streams

2003· article· en· W1980624937 on OpenAlexaff
Zarook Shareefdeen, Brian Herner, Derek Webb, Steve Wilson

Bibliographic record

VenueJournal of Industrial Microbiology & Biotechnology · 2003
Typearticle
Languageen
FieldChemical Engineering
TopicOdor and Emission Control Technologies
Canadian institutionsBiorem Technologies (Canada)University of Guelph
Fundersnot available
KeywordsBiofilterNuisanceOdorEnvironmental scienceWaste managementAir streamPulp and paper industryEnvironmental engineeringChemistryEngineeringEcologyBiology

Abstract

fetched live from OpenAlex

This paper focuses on recent developments of biofiltration technology used in treating nuisance chemical odors from industrial and municipal air streams. In the biofiltration process, odorous chemical constituents in the air are first transported to biofilms by diffusion, solubilization and adsorption processes. Bacteria within the biofilms oxidize odor constituents into harmless and odorless products. Through successful laboratory and pilot research on biofiltration of odorous air-stream constituents, numerous commercial biofilters have been designed and installed across North America. In this paper, case studies related to biofiltration of air emissions from meat rendering plants, municipal wastewater treatment applications, and printed circuit board production are discussed to demonstrate the robustness of this technology in eliminating a wide variety of compounds.

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)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
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 head0.026
GPT teacher head0.229
Teacher spread0.203 · 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

Citations31
Published2003
Admission routes1
Has abstractyes

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