MétaCan
Menu
Back to cohort
Record W6963591311 · doi:10.18739/a2h12v928

Cryostratigraphy and ground-ice content of the upper permafrost in Alaska and Northern Canada, 2018-2023

2024· dataset· en· W6963591311 on OpenAlexaboutno aff

Bibliographic record

VenueCalifornia Digital Library · 2024
Typedataset
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsnot available
Fundersnot available
KeywordsPermafrostWater contentBayArcticHydrology (agriculture)Soil waterMoisture

Abstract

fetched live from OpenAlex

This dataset contains information on cryostratigraphy and ground-ice content of the upper permafrost, which was based on the results of 22 field trips in 2018-2023. Field studies were performed in various regions of Alaska and Canadian Arctic including the following study areas: Utqiagvik (former Barrow), Teshekpuk Lake, Prudhoe Bay Oilfield, Toolik Lake, Jago River, Itkillik River, Anaktuvuk River, Fairbanks, Dalton Highway, Glennallen, Point Lay, Bylot Island (Canada), Inuvik-Tuktoyaktuk (Canada). Cryostratigraphy of the upper permafrost was studied mainly in coastal and riverbank exposures and frozen cores obtained from drilling with the SIPRE corer. Permafrost exposures and cores were described and photographed in the field, and obtained soil samples were delivered to the University of Alaska Fairbanks for additional descriptions and analyses. Ice contents of frozen soils (including gravimetric and volumetric moisture content, excess-ice content) were measured. The dataset includes cryostratigraphic descriptions, gravimetric (GMC) and volumetric (VMC) moisture content, excess-ice content (EIC), electrical conductivity (EC) and photographs of the permafrost exposures and frozen cores obtained from boreholes.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.025
Threshold uncertainty score0.063

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.007
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.002

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 head0.017
GPT teacher head0.181
Teacher spread0.164 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreDataset

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

Citations0
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueCalifornia Digital LibrarySame topicClimate change and permafrostFrench-language works237,207