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Record W2124900105 · doi:10.51847/1sihv8j

10.51847/1siHV8j

2000· article· en· W2124900105 on OpenAlexvenueno aff
Ibrahim M. Abu-Reesh

Bibliographic record

VenueTime to knit · 2000
Typearticle
Languageen
FieldEnvironmental Science
TopicMicrobial bioremediation and biosurfactants
Canadian institutionsnot available
FundersUniversity of Jordan
KeywordsPhenolVolume (thermodynamics)Continuous stirred-tank reactorBioreactorWastewaterChemistryBatch reactorChemical engineeringProcess engineeringPulp and paper industryEnvironmental engineeringEnvironmental scienceThermodynamicsOrganic chemistryEngineeringCatalysis

Abstract

fetched live from OpenAlex

An analytical relationship for the optimum design of continuous stirred tank reactors (CSTR’s) in series performing phenol degradation was derived. The optimal design is based on the minimum overall reactor volume required for a certain degree of phenol removal. It is assumed that cell growth kinetics follow the Haldanes kinetics model with respect to phenol and with no oxygen limitation. The effect of operating parameters such as phenol inlet feed concentration, phenol removal efficiency and

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.053
Threshold uncertainty score0.076

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.9470.938

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 head0.004
GPT teacher head0.160
Teacher spread0.156 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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

Citations5
Published2000
Admission routes1
Has abstractyes

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