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Record W7014616717

Radiogenic isotope geochemistry applied to the characterization of the provenance of sediments transported by icebergs during the last glacial period: A study in the Galicia Interior Basin

2017· article· en· W7014616717 on OpenAlexaboutno aff

Bibliographic record

VenuePortuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) · 2017
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsnot available
Fundersnot available
KeywordsProvenanceGlacial periodMeltwaterIcebergRadiogenic nuclideStructural basinFjordGlacier
DOInot available

Abstract

fetched live from OpenAlex

In the scope of a collaboration with the University of Vigo, the Geobiotec research unit has contributed with studies on the Sr and Nd isotopic fingerprints of the sediments deposited in the Galicia Interior Basin in the last six Heinrich Stadials (HS; climatic oscillations that culminated by massive discharge of icebergs to the North Atlantic during the last glacial period). Strongly negative ƐNd values during HS1 (~15-16 ka), HS2 (~23.5-25 ka), HS4 (~37.5-40 ka) and HS5(~43.8-45.5 ka) are consistent with a Canadian source for the sediments dropped by icebergs. In contrast, higher ƐNd and relatively low 87Sr/86Sr values were recorded during HS3, HS5a, HS6, but also in the initial stages of HS1 (~16-17.5 ka), HS2 (~25-26.3 ka) and HS4 (~40-42 ka), pointing to an European provenance of those sediments. The whole set of data suggests that large European meltwater discharges in the beginning of HS1, HS2 and HS4 could have contributed to the weakening of the Atlantic Meridional Overturning Circulation and, consequently, to the collapse of the ice sheets covering NW Europe and NE America.

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)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.089
Threshold uncertainty score0.178

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.030
GPT teacher head0.315
Teacher spread0.286 · 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
Published2017
Admission routes1
Has abstractyes

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