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Record W2100801425 · doi:10.3189/172756503781830944

Ice cores from Arctic sub-polar glaciers: chronology and post-depositional processes deduced from radioactivity measurements

2003· article· en· W2100801425 on OpenAlexaff
J. F. Pinglot, Rein Vaikmäe, Kokichi Kamiyama, Makoto Igarashi, Diedrich Fritzsche, Frank Wilhelms, Roy M. Koerner, Lori Henderson, Elisabeth Isaksson, Jan‐Gunnar Winther, Roderik S. W. van de Wal, Marc Fournier, P. Bouisset, Harro A. J. Meijer

Bibliographic record

VenueJournal of Glaciology · 2003
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsUniversity of OttawaGeological Survey of Canada
FundersNorsk PolarinstituttInternational Arctic Science Committee
KeywordsIce coreGlacierGeologySedimentary depositional environmentArcticChronologyPolarPhysical geographyGroenlandiaRadionuclideIsotopeIce sheetClimatologyGeomorphologyPaleontologyOceanography

Abstract

fetched live from OpenAlex

Abstract The response of Arctic ice masses to climate change is studied using ice cores containing information on past climatic and environmental features. Interpretation of this information requires accurate chronological data. Absolute dating of ice cores from sub-polar Arctic glaciers is possible using well-known radioactive layers deposited by atmospheric nuclear tests (maximum fallout in 1963) and the Chernobyl accident (1986). Analysis of several isotopes ( 3 H, 137 Cs) shows that 3 H provides the most accurate dating of the 1963 maximum, as indicated also in comparison with results from total-beta measurements ( 90 Sr and 137 Cs). Mean annual net mass balances are derived from the dated ice cores from 1963 up to the date of the drillings. The 137 Cs and 3 H deposited by nuclear tests, after decay correction, are used to define a melt index for all 13 ice cores studied. The relative strength of melting and percolation post-depositional processes is studied on the basis of these 137 Cs and 3 H deposits.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.008
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.029
GPT teacher head0.246
Teacher spread0.217 · 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

Citations39
Published2003
Admission routes1
Has abstractyes

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