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Record W4393783308 · doi:10.5281/zenodo.5706369

Data for Past terrestrial hydroclimate sensitivity controlled by Earth System Feedbacks

2022· dataset· en· W4393783308 on OpenAlexaff
Ran Feng, Tripti Bhattacharya, Bette L. Otto‐Bliesner, Esther C. Brady, Alan M. Haywood, Julia C. Tindall, Stephen J. Hunter, Ayako Ouchi, Wing‐Le Chan, Masa Kageyama, Camilie Contoux, Chuncheng Guo, Xiangyu Li, Gerrit Lohmann, Christian Stepanek, Ning Tan, Qiong Zhang, Zhongshi Zhang, Zixuan Han, Charles J. R. Williams, Daniel J. Lunt, Harry J. Dowsett, Deepak Chandan, W. R. Peltier

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2022
Typedataset
Languageen
FieldEarth and Planetary Sciences
TopicGeophysics and Gravity Measurements
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsEarth system scienceEnvironmental scienceGeologySensitivity (control systems)Earth scienceOceanographyEngineering

Abstract

fetched live from OpenAlex

This folder contains PlioMIP2 ensemble data and CESM2 simulations used in Feng et al., Past terrestrial hydroclimate sensitivity controlled by Earth System Feedbacks (2022). Here is some useful information: 1. Experiment IDs of CESM2 experiments are described in the Method section of the manuscript. The first dimension of variables in ensemble files reflects model IDs : "CCSM4", "CESM1", "CESM2", "COSMOS", "IPSL-CM6", "MIROC4m", "NorESM1-F", "HadCM3", "EC-Earth3.3", "IPSL-CM5", "IPSL-CM5A2", "HadGEM3", "GISS-E2-1G". 2. The naming convention of variables in ensemble files follows CMIP6 convention. The naming convention of CESM2 variables follows the convention of the model. 3. The script pe_budget_season_new.ncl produces moisture budget decomposition. The results are shown in Fig. 4 of the manuscript.

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 score0.047
Threshold uncertainty score0.156

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.004
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0470.043

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 head0.048
GPT teacher head0.237
Teacher spread0.189 · 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
Published2022
Admission routes1
Has abstractyes

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Same venueZenodo (CERN European Organization for Nuclear Research)Same topicGeophysics and Gravity MeasurementsFrench-language works237,207