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Record W3026775718 · doi:10.1038/s41597-020-0489-4

The Ontario Climate Data Portal, a user-friendly portal of Ontario-specific climate projections

2020· article· en· W3026775718 on OpenAlexafffundabout
Huaiping Zhu, Xiaolan Zhou, Xiaoyu Chen, Xin Qiu, Richard Bello, Ziwang Deng

Bibliographic record

VenueScientific Data · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsYork University
FundersNatural Sciences and Engineering Research Council of CanadaMinistère de l’Environnement, de la Protection de la nature et des ParcsCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada
KeywordsClimate changeUser FriendlyInternet portalGeographyPhysical geographyEnvironmental scienceEnvironmental resource managementComputer scienceWorld Wide WebData scienceEcologyBiologyThe Internet

Abstract

fetched live from OpenAlex

An easily accessible climate data portal, http://yorku.ca/ocdp, was developed and officially launched in 2018 to disseminate a super ensemble of high-resolution regional climate change projections for the province of Ontario, Canada. The spatial resolution is ~10 km × ~10 km and temporal resolution is one day, UTC. The data covers 120 years from 1981 to 2100. This user-friendly portal provides users with thousands of static and interactive maps, decadal variation trend lines, summary tables, reports and terabytes of bias-corrected downscaled data. The data portal was generated with an emphasis on interactive visualization of climate change information for researchers and the public to understand to what extent climate could change locally under different emission scenarios in the future. This paper presents an introduction to the portal structure and functions, the large extent of the datasets available and the data development methodology.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesOpen science, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.772
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0050.009
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0080.001

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 head0.093
GPT teacher head0.279
Teacher spread0.186 · 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.

Study designNot applicable
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

Citations12
Published2020
Admission routes3
Has abstractyes

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