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Record W4206517875 · doi:10.6000/1929-6037

Journal of Membrane and Separation Technology

2023· paratext· en· W4206517875 on OpenAlexvenueno aff

Bibliographic record

VenueJournal of Membrane and Separation Technology · 2023
Typeparatext
Languageen
FieldEngineering
TopicExtraction and Separation Processes
Canadian institutionsnot available
Fundersnot available
KeywordsSeparation (statistics)ChemistryComputer science

Abstract

fetched live from OpenAlex

The Journal of Membranes and Separation Technology is a peer-reviewed journal covers all aspects of membrane and separation sciences and technology. The Journal of Membranes and Separation Technology publishes high quality, original articles, review articles, case reports, field studies, and short communications as well as other scientific and educational information. The Journal of Membranes and Separation Technology facilitates the distribution and implementation of new ideas and techniques relating to synthesis and characterization of membrane and separation materials, filtration, fouling, module and operations, process design, processes simulation and applications with the ultimate aim of promoting the best practice. The Journal of Membranes and Separation Technology is addressed to both practicing professionals and researchers in the membranes and separation technology, professionals in academia, former researchers and PhD students and other specialists interested in the results of scientific research in separation sciences related to membranes.

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)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.933
Threshold uncertainty score0.223

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.004
Science and technology studies0.0020.002
Scholarly communication0.0100.005
Open science0.0010.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0670.043

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 head0.012
GPT teacher head0.291
Teacher spread0.279 · 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.

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
Published2023
Admission routes1
Has abstractyes

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Same venueJournal of Membrane and Separation TechnologySame topicExtraction and Separation ProcessesFrench-language works237,207