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Record W4393623593 · doi:10.5281/zenodo.7884563

Replication data for: Effect of Regional Marine Cloud Brightening Interventions on Climate Tipping Points

2023· dataset· en· W4393623593 on OpenAlexaff
Haruki Hirasawa, Dipti Hingmire, Hansi Singh, Philip J. Rasch, Peetak Mitra

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2023
Typedataset
Languageen
FieldEnvironmental Science
TopicCoastal and Marine Management
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsReplication (statistics)Climate changePsychological interventionCloud computingEnvironmental scienceClimatologyMeteorologyGeographyOceanographyComputer scienceGeologyPsychologyMathematicsStatistics

Abstract

fetched live from OpenAlex

Raw 2m temperature (TREFHT) and precipitation (PRECT) data from CESM2 MCB simulations. Tipping point metric timeseries from CESM2 LE Historical and SSP2-4.5 and CESM2 MCB simulations. Jupyter notebook displaying plotting script Scripts showing procedure for computing tipping point metrics

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)0.004
metaresearch head score (Gemma)0.022
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 score0.200
Threshold uncertainty score0.669

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.022
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0020.003
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0030.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.2000.095

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 head0.069
GPT teacher head0.303
Teacher spread0.233 · 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
Published2023
Admission routes1
Has abstractyes

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Same venueZenodo (CERN European Organization for Nuclear Research)→Same topicCoastal and Marine Management→French-language works237,207→