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Record W6931844859 · doi:10.5683/sp3/kt1dg5

Data For: Where do they come from, where do they go? Emissions and fate of OPEs in global megacities

2022· dataset· en· W6931844859 on OpenAlexaff

Bibliographic record

VenueBorealis · 2022
Typedataset
Languageen
FieldEngineering
TopicHeat Transfer and Numerical Methods
Canadian institutionsEnvironment and Climate Change CanadaUniversity of TorontoUniversity of British Columbia
Fundersnot available
KeywordsMegacityAir pollutionUrban areaUrbanizationMega-

Abstract

fetched live from OpenAlex

This data has been collected to parameterize the Multimedia Urban Model for 19 different mega or major cities. The data collected here can be used with the model, which is available from https://github.com/tfmrodge/FugModel, to estimate the transport and fate of organic contaminants from urban areas.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.052
Threshold uncertainty score0.108

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.004
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0320.038

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 head0.038
GPT teacher head0.317
Teacher spread0.279 · 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

Citations2
Published2022
Admission routes1
Has abstractyes

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