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Record W4393572725 · doi:10.5281/zenodo.7378326

Data set for: "Model atmospheric aerosols convert to vesicles upon entry into aqueous solution"

2022· dataset· en· W4393572725 on OpenAlex
Serge Nader, Alexandre Baccouche, Fiona Connolly, Desmond Pink, Sheref S. Mansy

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

VenueZenodo (CERN European Organization for Nuclear Research) · 2022
Typedataset
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicDiffusion and Search Dynamics
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsAqueous solutionSet (abstract data type)VesicleChemistryData setEnvironmental scienceBiological systemAtmospheric sciencesChemical engineeringMathematicsComputer sciencePhysicsOrganic chemistryStatisticsBiologyEngineeringBiochemistryMembrane

Abstract

fetched live from OpenAlex

This document compiles raw data used in the aerosol to vesicle transformation study carried out by <strong>Serge Nader <em>et al.</em></strong><br> For detailed information and context, refer to the main article and its supplementary material published in ACS Earth and Space Chemistry. The Excel file contains data relevant to each figure in the main article and supporting information. The additional compressed file contains raw Transmission Electron Microscopy (TEM) photographs.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Open science, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.013
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0010.000
Open science0.0030.009
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0130.001

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 head0.042
GPT teacher head0.287
Teacher spread0.245 · 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