MétaCan
Menu
Back to cohort
Record W4281482829 · doi:10.1016/j.dib.2022.108298

Cold region data accessibility portal for Québec (CRDAP-QC): An integrated, multi-variable and multi-scale data repository for studying cold-region hydrological processes in Québec

2022· article· en· W4281482829 on OpenAlexafffundabout
Ali Nazemi, Shakil Jiwa, Shadi Hatami

Bibliographic record

VenueData in Brief · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsMcGill UniversityConcordia University
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of CanadaConcordia University
KeywordsSnowData archiveUploadSnow coverScale (ratio)Land coverEnvironmental scienceRaw dataPrecipitationElevation (ballistics)Data setStructural basinVariable (mathematics)Remote sensingDatabaseMeteorologyComputer scienceGeographyCartographyLand useGeologyEcologyWorld Wide Web

Abstract

fetched live from OpenAlex

We present an integrated data portal and retrieval system for various variable related to hydrology and cryosphere of Québec, named Cold Region Data Accessibility Portal for Québec (CRDAP-QC). The raw data with which this integrated platform is built are pulled from various publicly available data sources. The platform integrates data variables related to climate (maximum, minimum and mean temperature along with total precipitation), snow accumulation (snow cover and depth) as well as Freeze-Thaw characteristics. The platform enables downloading, visualizing, and comparing these data across various temporal (monthly, seasonal and annual) and spatial scales (from 25 × 25 km2 grids, to sub-basin and basin, to the whole province). The platform also provides a Printable Document File with summary of the data, an additional set of information on elevation, land-use, and land-cover as well as the location of climate and hydrometric stations within the chosen area. The portal is available in both English and French.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.799
Threshold uncertainty score0.878

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.002
Open science0.0030.001
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 head0.211
GPT teacher head0.328
Teacher spread0.117 · 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 designObservational
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

Citations2
Published2022
Admission routes3
Has abstractyes

Explore more

Same venueData in BriefSame topicClimate change and permafrostFrench-language works237,207