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Record W2328163743 · doi:10.1021/es301721f

Colloid Transport in Dolomite Rock Fractures: Effects of Fracture Characteristics, Specific Discharge, and Ionic Strength

2012· article· en· W2328163743 on OpenAlexaff
Pulin K. Mondal, Brent E. Sleep

Bibliographic record

VenueEnvironmental Science & Technology · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicGroundwater flow and contamination studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsDolomiteIonic strengthColloidDiffusionMineralogyChemistryMaterials scienceChemical engineeringThermodynamicsAqueous solution

Abstract

fetched live from OpenAlex

The effects of fracture characteristics, specific discharge, and ionic strength on microsphere transport in variable-aperture dolomite rock fractures were studied in a laboratory-scale system. Fractures with different aperture distributions and mineral compositions were artificially created in two dolomite rock blocks. Transport tests were conducted with bromide and carboxylate-modified latex microspheres (20, 200, and 500 nm diameter). Under overall unfavorable attachment conditions, there was significant retention of the 20 nm microsphere and minimal retention of the 500 nm microsphere for all conditions examined. Aperture variability produced significant spatial variation in colloid transport. Flushing with low ionic strength solution (1 mM) following microsphere transport at 12 mM ionic strength solution produced a spike in effluent microsphere concentrations, consistent with retention of colloids in secondary energy minima. Surface roughness and charge heterogeneity effects may have also contributed to the effect of microsphere size on retention. Matrix diffusion influenced bromide transport but was not a dominant factor in transport for any microsphere size. Calibrated one-dimensional, two-site kinetic model parameters for colloid transport in fractured dolomite were sensitive to the physical and chemical properties of both the fractured dolomite and the colloids, indicating the need for mechanistic modeling for accurate prediction.

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 score0.099
Threshold uncertainty score0.689

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.003
GPT teacher head0.196
Teacher spread0.193 · 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

Citations46
Published2012
Admission routes1
Has abstractyes

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