MétaCan
Menu
Back to cohort
Record W4366528586 · doi:10.1002/cben.202200012

Overview of Fouling – An Industrial Jeopardy

2023· article· en· W4366528586 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueChemBioEng Reviews · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicMembrane Separation Technologies
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsFoulingBiochemical engineeringMembrane foulingEnvironmental scienceProcess engineeringForensic engineeringComputer scienceMembraneEngineeringChemistry

Abstract

fetched live from OpenAlex

Abstract A brief insight into fouling and its various types, which impose a hurdle on the performance of an apparatus and ultimately a negative impact on the environment as a whole due to the excess unwanted deposits, is given. Within the various types mentioned, specific cases of fouling occurrences in each are also highlighted. The mechanism associated with the specific cases as well as the difficulties arising are mentioned as interpreted from previous literature and experiments. There are cases where more than one type of fouling is found to be simultaneously occurring, which is referred to as composite fouling. Numerous parameters control this phenomenon, along with some exceptions associated with the same. Few membranes or plates or their physical aspects prove to be quite effective in withstanding or overcoming a specific type of fouling. Mitigation of fouling demands proper monitoring and control of the chemical processes to ensure lesser damage to the equipments. Industrial fouling in general and specific scenarios of fouling are overviewed and discussed.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.694
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.002

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 head0.259
GPT teacher head0.368
Teacher spread0.108 · 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