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Record W2318150701 · doi:10.1144/m40.16

Chapter 16 The rock coasts of polar and sub-polar regions

2014· article· en· W2318150701 on OpenAlexaff
James D. Hansom, Donald L. Forbes, Samuel Étienne

Bibliographic record

VenueGeological Society London Memoirs · 2014
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsBedford Institute of OceanographyGeological Survey of CanadaMemorial University of Newfoundland
Fundersnot available
KeywordsGeologySea iceSeabed gouging by iceOceanographyAntarctic sea iceArctic ice packIce sheetIcebergRock glacierFast iceGlacierPermafrostArcticDrift iceGlacial periodCryosphereShoreGeomorphology

Abstract

fetched live from OpenAlex

Abstract Polar and subpolar coasts are distinctive owing to the presence of ice on land as permafrost, ground ice and glacier ice, and in the sea as tidewater glaciers, icebergs, ice shelves and sea ice. Most of these coasts remain glaciated or are recently deglaciated so their geomorphology carries a strong glacial signature. The morphogenetic environment of polar and subpolar coasts is dominated by extreme seasonality with winter development of sea ice and a shore-fast ice foot that excludes wave activity and is primarily protective. However, sea ice may also be erosional at any time of year but is most effective as an erosional agent on polar coasts between freeze-up and break-up, when wave activity forces sea ice to repeatedly impact the shore. Depending on latitude, the short summers are characterized by wave and sea ice erosion at high latitudes and by wave activity at lower latitudes. The contribution of frost weathering to cliff and shore platform development in polar and subpolar rock coasts is unclear, but is likely to be an important influence. Rock coasts are widespread in the Arctic and sub-Arctic, including Iceland, and in the Antarctic and sub-Antarctic the limited ice-free coast is almost entirely rock-dominated.

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 score0.101
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 head0.025
GPT teacher head0.215
Teacher spread0.190 · 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

Citations13
Published2014
Admission routes1
Has abstractyes

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