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Record W4243085957 · doi:10.1061/9780784414088.ch12

Behavior and Fate of Natural and Engineered Nanomaterials in Sediments

2015· book-chapter· en· W4243085957 on OpenAlexaff
Karima Gmiza, Nam Nghiep Tran, Satinder Kaur Brar, Patrick Drogui, Guy Mercier, Jean‐François Blais

Bibliographic record

VenueAmerican Society of Civil Engineers eBooks · 2015
Typebook-chapter
Languageen
FieldMaterials Science
TopicNanoparticles: synthesis and applications
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsSedimentationBiotaSedimentNanomaterialsNatural (archaeology)ChemistryNanotechnologyGeologyMaterials scienceEcologyBiologyGeomorphologyPaleontology

Abstract

fetched live from OpenAlex

This chapter discusses the behavior and fate of natural and engineered nanomaterials in sediments, beginning with a discussion of the origin and nature of the sediments. Nanoparticles (NPs) are persistent in sediments. They have low solubility under normal conditions, are hydrophobic, tend to form aggregates, and are not biodegradable as they accumulate in aquatic biota. In addition to persistence, the chapter presents a general picture of the mechanisms of NP transport from water to sediment. Aggregation seems to be the main route for sedimentation and can occur as homoaggregation (between NPs) or heteroaggregation (with other particles). Understanding the fate of NPs in sediments would shed light on a large range of nanotechnology-related problems.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.017
GPT teacher head0.239
Teacher spread0.222 · 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.

Study designBench or experimental
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

Citations0
Published2015
Admission routes1
Has abstractyes

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