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Record W4405586041 · doi:10.1016/j.dib.2024.111246

Spatial datasets of CMIP6 climate change projections for Canada and the United States

2024· article· en· W4405586041 on OpenAlexaffabout
Daniel W. McKenney, John Pedlar, Kevin Lawrence, S. R. Sobie, Kaitlin DeBoer, Tiziana Brescacin

Bibliographic record

VenueData in Brief · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsPacific Institute for Climate SolutionsUniversity of VictoriaOntario Forest Research InstituteNatural Resources Canada
Fundersnot available
KeywordsClimate changeClimatologyEnvironmental scienceGeographyGeologyEcologyBiology

Abstract

fetched live from OpenAlex

Geospatial climate change projections are critical for assessing climate change impacts and adaptations across a wide range of disciplines. Here we present monthly-based grids of climate change projections at a 2-km resolution covering Canada and the United States. These data products are based on outputs from the 6th Coupled Model Intercomparison Project (CMIP6) and include projections for 13 General Circulation Models (GCMs), three Shared Socio-economic Pathways (SSP1 2.6, SSP2 4.5, and SSP5 8.5), four 30-year time periods (2011-2040, 2021-2050, 2041-2070, and 2071-2100), and a suite of climate variables, including monthly maximum and minimum temperature, precipitation, climate moisture index, and various bioclimatic summaries. The products employ a delta downscaling method, which combines historical normal values at climate stations with broad-scale change projections (or deltas) from GCMs, followed by spatial interpolation using ANUSPLIN. Various quality control efforts, described herein, were undertaken to ensure that the final products provided reasonable estimates of future climate.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.625
Threshold uncertainty score0.246

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.019
GPT teacher head0.244
Teacher spread0.225 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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

Citations3
Published2024
Admission routes2
Has abstractyes

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