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Record W3014810731 · doi:10.3390/min10040317

Analysis of Kaolin Flocculation in Seawater by Optical Backscattering Measurements: Effect of Flocculant Management and Liquor Conditions

2020· article· en· W3014810731 on OpenAlexaff
Ricardo I. Jeldres, Matías Jeldres, Michael R. MacIver, Marek Pawlik, Pedro Robles, Norman Toro

Bibliographic record

VenueMinerals · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicGroundwater flow and contamination studies
Canadian institutionsUniversity of British Columbia
FundersComisión Nacional de Investigación Científica y TecnológicaCentro de Recursos Hídricos para la Agricultura y la MineríaPontificia Universidad Católica de ValparaísoMinisterio de Educación, Gobierno de Chile
KeywordsFlocculationSeawaterElectrolyteChemistryParticle sizeMineralogyCoagulationParticle aggregationAnalytical Chemistry (journal)Materials scienceEnvironmental chemistryGeologyNanoparticle

Abstract

fetched live from OpenAlex

Optical backscattering (OBS) signal values were used to evaluate the flocculation of kaolin slurries and seek the implications of using seawater. Two anionic flocculants were applied to kaolin suspensions at several dosages and in water of varying pH and electrolyte concentration. An OBS height scan method was used to estimate the degree of aggregation, supernatant quality, and solids concentration of the sediments. The residual solids of the supernatant depended of the extent of particle coagulation before flocculant was added, where the pH and salinity displayed a significant impact on flocculation. The OBS results were highly sensitive to the presence of fine particles, which was estimated in parallel from the focused beam reflectance measurements (FBRM.) In seawater, without flocculant added, the samples had increased root-mean-square scattering (Frms) values and larger final sediment volume than samples prepared in water with lower electrolyte concentration. This indicates a higher initial state of aggregation of the particles in seawater. Then, the aggregation degree was best linked to the square-weighted chord length distribution of the FBRM data, which intensifies the sensitivity to coarse aggregates.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.266
Threshold uncertainty score0.253

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 head0.017
GPT teacher head0.246
Teacher spread0.229 · 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 designObservational
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

Citations8
Published2020
Admission routes1
Has abstractyes

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