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Record W1803497776 · doi:10.5194/tc-10-553-2016

A comprehensive interpretation of the NEEM basal ice build-up using a multi-parametric approach

2016· article· en· W1803497776 on OpenAlexfundno aff
Thomas Goossens, Célia Sapart, Dorthe Dahl‐Jensen, Trevor Popp, Saïda El Amri, Jean‐Louis Tison

Bibliographic record

Venue˜The œcryosphere · 2016
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsnot available
FundersNatural Environment Research CouncilFonds Wetenschappelijk OnderzoekNatural Resources CanadaCentre National de la Recherche ScientifiqueFonds pour la Formation à la Recherche dans l’Industrie et dans l’AgricultureKorea Polar Research InstituteAgence Nationale de la RechercheNederlandse Organisatie voor Wetenschappelijk OnderzoekInstitut Polaire Français Paul Emile VictorNational Institute of Polar ResearchOffice of Polar ProgramsFonds De La Recherche Scientifique - FNRSSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungNational Science Foundation
KeywordsGeologyDebrisIce coreIce streamIce sheetIce divideδ18OGeomorphologySea iceStable isotope ratioCryosphereOceanography

Abstract

fetched live from OpenAlex

Basal ice is a common expression to describe bottom ice layers of glaciers, ice caps and ice sheets in which the ice is primarily conditioned by processes operating at the bed. It is chemically and/or physically distinct from the ice above and can be characterized by a component of basally derived sediments. The study of basal ice properties provides a rare opportunity to improve our understanding of subglacial environments and processes and to refine ice sheet behaviour modelling. Here, we present and discuss the results of water stable isotopes ( δ 18 O and δ D), ice fabrics, debris weight/size distribution and gas content of the basal part of the NEEM (North Greenland Eemian Ice Drilling Project) ice core. Below a depth of 2533.85 m, almost 10 m of basal debris-rich material was retrieved from the borehole, and regular occurrence of frozen sediments with only interstitial ice lenses in the bottom 5 m suggest that the ice–bedrock interface was reached. The sequence is composed of an alternation of three visually contrasting types of ice: clear ice with specks (very small amounts) of particulate inclusions, stratified debris-rich layers and ice containing dispersed debris. The use of water stable isotope signatures ( δ 18 O and δ D), together with other parameters, allows discrimination between the different types of ice and to unravel the processes involved in their formation and transformation. The basal debris-rich material presents δ 18 O values [−39.9 ‰; −34.4 ‰] within the range of the above last 300 m of unaltered meteoric ice [−44.9 ‰; −30.6 ‰] spanning a glacial-interglacial range of values. This rules out the hypothesis of a basal ice layer originating from pre-ice sheet ice overridden by the growing ice sheet, as previously suggested e.g. in the case of GRIP (Greenland Ice Core Project). We show that clear basal ice with specks corresponds to altered meteoric glacial ice where some of the climatic signal could have been preserved. However, the stratified debris-rich layers and the ice containing dispersed debris layers respectively express an “open” or “closed” system melting/refreezing signature, somewhat blurred by mixing processes in the upper part of the sequence. Climatic reconstruction is therefore prohibited from these ice types. We propose a first interpretative framework for the build-up of the NEEM basal ice sequence, based on the origin of the various ice types.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.039
GPT teacher head0.243
Teacher spread0.204 · 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

Citations9
Published2016
Admission routes1
Has abstractyes

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