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Record W4237295493 · doi:10.4324/9781351282086-7

Physicochemical Characterisation and Recycling of Industrial Residues

2017· book-chapter· en· W4237295493 on OpenAlexaboutno aff
Maurice Morency, Denise Fontaine, Guoji Shan

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldEngineering
TopicExtraction and Separation Processes
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessChemistry

Abstract

fetched live from OpenAlex

The Environmental Research Centre at the University of Quebec at Montreal (CREUST) is located in an industrial area of Quebec. CREUST is an initiative that has resulted from local efforts, a genuine willingness to improve environmental conditions in the region and an attempt to diversify and stimulate employment in the area. The research projects have involved the characterisation and handling of fine to submicron-sized particles in order to solve associated environmental problems. The laboratories are equipped with specialised apparatus for performing exhaustive physical characterisation of soils, mining and industrial residues. The generation of such solid particles is common to most industrial processes and no conventional physical separation techniques are readily applicable for such materials when an environmental problem is identified and a solution is required. A chemical analysis of the slurry showed high concentrations of zinc, lead and other valuable metals such as rhenium and silver.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.774
Threshold uncertainty score0.476

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.053
GPT teacher head0.265
Teacher spread0.212 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2017
Admission routes1
Has abstractyes

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