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Record W2898869531 · doi:10.1039/c8en00386f

Searching for relevant criteria to distinguish natural <i>vs.</i> anthropogenic TiO<sub>2</sub> nanoparticles in soils

2018· article· en· W2898869531 on OpenAlexaff
Hiram Castillo‐Michel, Rälf Kaegi, Camille Larue, Wout De Nolf, Juan Reyes-Herrera, Rémi Tucoulou, Nathaniel Findling, Eduardo Salas‐Colera, Géraldine Sarret

Bibliographic record

VenueEnvironmental Science Nano · 2018
Typearticle
Languageen
FieldMaterials Science
TopicNanoparticles: synthesis and applications
Canadian institutionsInstitute of Particle Physics
FundersConsejo Nacional de Ciencia y TecnologíaEidgenössische Anstalt für Wasserversorgung Abwasserreinigung und GewässerschutzEuropean Cooperation in Science and TechnologyEuropean Synchrotron Radiation FacilityAgence Nationale de la RechercheLabEx SERENADEEquipex
KeywordsSoil waterEnvironmental chemistryNanoparticleNatural (archaeology)Environmental scienceMaterials scienceMineralogyChemical engineeringSoil scienceChemistryNanotechnologyGeology

Abstract

fetched live from OpenAlex

The morphology of TiO 2 particles and their status within soil aggregates may provide insights on their origin.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0050.002
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.013
GPT teacher head0.277
Teacher spread0.265 · 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.

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

Citations40
Published2018
Admission routes1
Has abstractyes

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