MétaCan
Menu
Back to cohort
Record W2770581687 · doi:10.1002/adsu.201770111

Carbon Dioxide Conversion: Tailoring CO<sub>2</sub> Reduction with Doped Silicon Nanocrystals (Adv. Sustainable Syst. 11/2017)

2017· article· en· W2770581687 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAdvanced Sustainable Systems · 2017
Typearticle
Languageen
FieldEngineering
TopicCarbon Dioxide Capture Technologies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsDopantMaterials scienceHydrideNanocrystalSiliconCarbon fibersDopingBoronNanotechnologySolar fuelChemical engineeringCatalysisOptoelectronicsPhotocatalysisChemistryMetallurgyComposite numberOrganic chemistryEngineering

Abstract

fetched live from OpenAlex

The conversion of CO2 into solar fuels usually requires unsustainable materials. In article number 1700118, Geoffrey A. Ozin and co-workers demonstrate gas-phase CO2 reduction using hydride-terminated boron- and phosphorus-doped silicon nanocrystals, which consist of earth-abundant and non-toxic elements. The CO2 reduction activity of silicon nanocrystals is successfully doubled with the addition of dopants. Cover illustrator: Chenxi Qian.

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.

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)0.001
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 score0.157
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.002
Open science0.0010.000
Research integrity0.0000.001
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 head0.007
GPT teacher head0.210
Teacher spread0.203 · 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