MétaCan
Menu
Back to cohort
Record W4398554430 · doi:10.7910/dvn/hbikkv

Data support for "Impact of CCN hygroscopicity and turbulence on cloud droplet growth: An in-cloud seeding case study using parcel-DNS approach"

2019· dataset· en· W4398554430 on OpenAlexaff
Sisi Chen, Lulin Xue, M. K. Yau

Bibliographic record

VenueHarvard Dataverse · 2019
Typedataset
Languageen
FieldEarth and Planetary Sciences
TopicAeolian processes and effects
Canadian institutionsMcGill University
Fundersnot available
KeywordsCloud computingSeedingTurbulenceMeteorologyEnvironmental scienceAtmospheric sciencesMaterials scienceComputer sciencePhysicsThermodynamicsOperating system

Abstract

fetched live from OpenAlex

This dataset provide the DNS model output for studying aerosol-cloud interaction and impact of aerosols in cloud development and rain initiation. The relevant manuscript is under review on ACPD: https://doi.org/10.5194/acp-2019-886.

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)0.003
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 score0.053
Threshold uncertainty score0.176

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0020.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0020.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0530.048

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 head0.078
GPT teacher head0.316
Teacher spread0.238 · 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

Citations1
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueHarvard DataverseSame topicAeolian processes and effectsFrench-language works237,207