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Record W6929576745 · doi:10.48577/jpl.axq6zm

Glacial Vermicular Ridge Features on Axel Heiberg Island, Nunavut, Canada

2025· dataset· en· W6929576745 on OpenAlexaboutno aff

Bibliographic record

VenueJPL Data · 2025
Typedataset
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBacterial Genetics and Biotechnology
Canadian institutionsnot available
Fundersnot available
KeywordsFjordGlacial periodMoraineRidgeDebrisGlacierMeltwaterGlacial lake

Abstract

fetched live from OpenAlex

Vermicular Ridge Features (VRFs) comprise a series of ridges and troughs with a circular, sinuous, and anastomosing morphology composed of clast-rich sandy diamict. VRFs were first reported on the south coast of Devon Island, Nunavut, Canada, in the Dundas Harbour region. Here, we document the presence of VRFs near Mokka Fjord on Axel Heiberg Island, Nunavut, Canada. We utilize field observations, ultra high resolution LiDAR, and ground penetrating radar to characterize and compare the morphometry and sedimentology of VRFs near Mokka Fjord on the eastern coast of Axel Heiberg Island to other periglacial, paraglacial, and glacial landforms. A leading periglacial (i.e., segregation ice features/lithalsas) and glacial (i.e., ring-ridge moraines and kame/kettled terraces) origin are presented. We interpret Mokka Fjord VRFs to be an ice-marginal feature resulting from paraglacial ablation of buried glacial ice producing a hummocky ring-ridge moraine comprised of supra- and englacial debris. This formation mechanism would infer a largely polythermal glacial environment with limited water supply. Likely from occasional warm-based periods at the ice margins which may allow sediment output and ice burial from basal ice debris redistribution or the thinning and subsequent burial of snout ice from glaciofluvial outwash.

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)0.000
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: none
Teacher disagreement score0.011
Threshold uncertainty score0.063

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0030.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.008
GPT teacher head0.242
Teacher spread0.233 · 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
Published2025
Admission routes1
Has abstractyes

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