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Record W7019435374

Glaciated margins: the sedimentary and geophysical archive

2019· other· en· W7019435374 on OpenAlexaboutno aff

Bibliographic record

VenueNERC Open Research Archive (Natural Environment Research Council) · 2019
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsIce sheetSedimentary rockSedimentary depositional environmentGround-penetrating radarCryosphereStructural basinSedimentary basin
DOInot available

Abstract

fetched live from OpenAlex

Understanding the sedimentary and geophysical archive of glaciated margins is a complex task that requires integration and analysis of disparate sedimentological and geophysical data. Their analysis is vital for understanding the dynamics of past ice sheets and how they interact with their neighbouring marine basins, on timescales that cannot be captured by observations of the cryosphere today. As resources, sediments deposited on the inner margins of glaciated shelves also exhibit resource potential where more sand-dominated systems occur, acting as reservoirs for both hydrocarbons and water. This book surveys the full gamut of glaciated margins, from deep time (Neoproterozoic, Ordovician and Carboniferous–Permian) to modern high-latitude margins in Canada and Antarctica. This collection of papers is the first attempt to deliberately do this, allowing not only the similarities and differences between modern and ancient glaciated margins to be explored, but also the wide spectrum of their mechanisms of investigation to be probed. Together, these papers offer a high-resolution, spatially and temporally diverse blueprint of the depositional processes, ice sheet dynamics, and basin architectures of the world’s former glaciated margins; a vital resource in advancing understanding of our present and future marine-terminating ice sheet margins.

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)0.001
metaresearch head score (Gemma)0.004
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.102
Threshold uncertainty score0.340

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0060.016
Science and technology studies0.0010.001
Scholarly communication0.0040.003
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.1020.141

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 head0.108
GPT teacher head0.347
Teacher spread0.239 · 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
Published2019
Admission routes1
Has abstractyes

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Same venueNERC Open Research Archive (Natural Environment Research Council)French-language works237,207