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Record W4399878377 · doi:10.1139/as-2023-0058

The Canadian Permafrost Electrical Resistivity Survey (CPERS) database: 15 years of permafrost resistivity data

2024· article· en· W4399878377 on OpenAlexafffundvenueabout
Teddi Herring, Antoni G. Lewkowicz, Alexandre Chiasson, Yifeng Wang, Robert G. Way, Joseph M. Young, Duane Froese, Sharon L. Smith, Brielle Andersen, O Bellehumeur-Génier, Alexandre Bevington, Philip P. Bonnaventure, Maxime Duguay, Bernd Etzelmüller, M. N. Gooseff, Sarah E. Godsey, Christina M Miceli

Bibliographic record

VenueArctic Science · 2024
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsUniversity of LethbridgeGovernment of British ColumbiaTransport CanadaGeological Survey of CanadaUniversity of AlbertaNatural Resources CanadaQueen's UniversityUniversity of CalgaryBGC Engineering (Canada)University of Ottawa
FundersNatural Sciences and Engineering Research Council of CanadaAlliance de recherche numérique du CanadaInternational Permafrost Association
KeywordsPermafrostElectrical resistivity tomographyMetadataDatabasePhysical geographyElectrical resistivity and conductivityGeologyEarth scienceGeographyComputer scienceOceanographyEngineering

Abstract

fetched live from OpenAlex

Permafrost landscapes are becoming increasingly susceptible to widespread thaw due to climate change. Collating historical and ongoing data are critical for assessing permafrost conditions and spatiotemporal changes. Electrical resistivity tomography (ERT) is a geophysical technique that has become standard practice for characterizing permafrost. However, resistivity data—particularly raw measurements—often go unpublished and unshared, resulting in missed opportunities for knowledge exchange and collaboration. To fill this gap, we created the Canadian Permafrost Electrical Resistivity Survey database and established clear guidelines for data archival and reuse. Here, we present the first release of the database, which currently houses 280 ERT datasets, including standardized metadata, collected between 2008 and 2022 in British Columbia, Labrador, Northwest Territories, Québec, Yukon, and Alaska. These data present unique opportunities to better understand spatial and temporal variability of permafrost conditions across North America.

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)0.006
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.266
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0020.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.087
GPT teacher head0.301
Teacher spread0.214 · 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 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
Published2024
Admission routes4
Has abstractyes

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