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Record W4393700206 · doi:10.5281/zenodo.4337092

Data archive for the peer-reviewed journal article "Detailed characterization of the CAPS single scattering albedo monitor (CAPS PMssa) as a field-deployable instrument for measuring aerosol light absorption with the extinction-minus-scattering method"

2020· dataset· en· W4393700206 on OpenAlexaff
Robin L. Modini, Joel C. Corbin, Benjamin T. Brem, M. Irwin, Michele Bertó, Rosaria E. Pileci, Podromos Fetfatzis, Bas Henzing, M. Moerman, Fengshan Liu, Thomas Müller, Martin Gyesl-Beer

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2020
Typedataset
Languageen
FieldEnvironmental Science
TopicAtmospheric aerosols and clouds
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsSingle-scattering albedoExtinction (optical mineralogy)Characterization (materials science)AerosolScatteringAlbedo (alchemy)Absorption (acoustics)Remote sensingField (mathematics)Environmental sciencePhysicsOpticsGeographyMeteorologyHistory

Abstract

fetched live from OpenAlex

Data archive accompanying the peer-reviewed journal article "Detailed characterization of the CAPS single scattering albedo monitor (CAPS PMssa) as a field-deployable instrument for measuring aerosol light absorption with the extinction-minus-scattering method". In 2020 this article was accepted for publication in the journal Atmospheric Measurement Techniques. Data are uploaded in the form of ascii text files, Igor Pro experiment files (.pxp), and Jupyter notebook files. In addition, a Jupyter notebook file is included containing an implementation of the error model used in the paper.

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)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.109
Threshold uncertainty score0.364

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.005
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0030.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.1090.172

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 head0.043
GPT teacher head0.246
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

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 designNot applicable
Domainnot available
GenreDataset

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
Published2020
Admission routes1
Has abstractyes

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Same venueZenodo (CERN European Organization for Nuclear Research)→Same topicAtmospheric aerosols and clouds→French-language works237,207→