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Record W4366465541 · doi:10.1080/17445647.2023.2203333

The glacial geomorphology of the Mackenzie Mountains region, Canada

2023· article· en· W4366465541 on OpenAlexaffabout
Benjamin J. Stoker, Martin Margold, Duane Froese

Bibliographic record

VenueJournal of Maps · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsUniversity of Alberta
FundersGrantová Agentura, Univerzita KarlovaUniverzita Karlova v Praze
KeywordsDeglaciationGeologyGlacial periodIce sheetGlacierPhysical geographyGlacial landformWisconsin glaciationGlacier morphologyGeomorphologyIce streamCryosphereMoraineOceanographyGeographySea ice

Abstract

fetched live from OpenAlex

During the Last Glacial Maximum, the Mackenzie Mountains region was glaciated by three distinct ice sources; the Laurentide Ice Sheet, the Cordilleran Ice Sheet, and independent montane glaciers. Rapid ice sheet thinning of the Laurentide-Cordilleran ice saddle in the south of this region contributed to rapid sea level rise events and influenced the style of deglaciation to the north. The current understanding of the glacial history of the broader region has been established through mapping from aerial imagery and early surveys between the early 1970s to the 2010s. The central portions of the Mackenzie Mountains have not yet been mapped. We present a new glacial geomorphological map for the Mackenzie Mountains region covering over 220,000 km2. This updated geomorphological map will form the basis of future work to reconstruct the former maximum ice extents, flow dynamics, and retreat pattern.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.106

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.005
Science and technology studies0.0020.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.018
GPT teacher head0.202
Teacher spread0.184 · 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 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

Citations0
Published2023
Admission routes2
Has abstractyes

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