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Borehole and near-surface ground temperatures in northeastern Canada

2024· article· en· W2929143158 on OpenAlexaffabout
Michel Allard, Denis Sarrazin, E. L'Hérault

Bibliographic record

VenueNordicana D · 2024
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsCenter for Northern Studies
Fundersnot available
KeywordsPermafrostBoreholeTerrainClimate changeEnvironmental scienceTable (database)GeologyPhysical geographyForcing (mathematics)Hydrology (agriculture)ClimatologyGeographyOceanographyCartography

Abstract

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Permafrost is the foundation upon which northern ecosystems and communities rest and upon which new industrial infrastructures are built. Determining the thermal state and dynamics of permafrost soils is therefore of broad interest to many disciplines and users. The purpose of this project is to evaluate the thermo-dynamical processes in various geological, climatic, municipal and industrial settings that lead to permafrost thaw from climate forcing and terrain disturbance. The data are from 11 sets of locations in Nunavik and Nunavut, northeastern Canada. The measurement sites are part of the SILA network of the Center for Northern Studies (CEN - Centre d’études nordiques); climate data are also available in other issues of Nordicana D. The available datasets cover the period from 1988-2023. For each set of locations, the number of boreholes and their depth ranges are as follows: Akulivik: 2 sites, Aupaluk: 2 sites, Kangiqsualujjuaq: 2 sites, Puvirnituq: 2 sites, Quaqtaq: 1 site, Tasiujaq: 2 sites, Umijuaq: 1 site, Iqaluit: 6 sites, Pangnirtung: 8 sites, Île Bylot 3 sites. In Salluit, the temperature data were recorded by Hobo temperature loggers near the ground surface at 21 different sites. Table 1 shows the depth range for the sites. The ground temperature data (oC) are available as: (1) recorded data, which are the averages of 60 measurements at minute intervals over each previous hour; (2) daily averages; (3) monthly averages; and (4) yearly averages.

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 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 score0.081
Threshold uncertainty score0.999

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)0.0020.000

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 head0.016
GPT teacher head0.217
Teacher spread0.201 · 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

Citations44
Published2024
Admission routes2
Has abstractyes

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