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Record W7110445338 · doi:10.4324/9781003470977-28

A numerical model of postglacial relative sea level change near Baffin Island

2024· book-chapter· en· W7110445338 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsnot available
Fundersnot available
KeywordsSea levelPost-glacial reboundPleistoceneIce sheetSea level changeArctic ice packLast Glacial MaximumIce age

Abstract

fetched live from OpenAlex

It is intuitive that some form of causal relationship must exist between the ablation of late Pleistocene ice sheets and the variations in RSL (relative sea level) seen preserved in the geologic record of coastal areas. The form of this relationship has been quantified in numerical models which successfully explain the major features of the global postglacial RSL record ( Farrell and Clark, 1976 ; Peltier and Andrews, 1976 ). By increasing the spatial resolution of these models, Quinlan and Beaumont (1981 , 1982 ) examined details of the RSL record within Atlantic Canada and proposed a compatible late Wisconsin ice reconstruction for that area. The current paper uses the Quinlan and Beaumont (1982) approach to analyse the RSL record of the region around Baffin Island. There are two principal objectives: first, to derive an ice reconstruction consistent with the known RSL record; and second, to use this reconstruction to estimate the form of the RSL record for sites where direct RSL Indicators are either absent or anbiguous in their implications.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.760
Threshold uncertainty score0.478

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.100
GPT teacher head0.269
Teacher spread0.169 · 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 designSimulation or modeling
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
Published2024
Admission routes1
Has abstractyes

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