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Record W1693477123 · doi:10.1029/2002pa000795

Oversampling of sedimentary series collected by giant piston corer: Evidence and corrections based on 3.5‐kHz chirp profiles

2004· article· en· W1693477123 on OpenAlexfundno aff
Nadia Széréméta, Franck Bassinot, Yvon Balut, Laurent D Labeyrie, Maurice Pagel

Bibliographic record

VenuePaleoceanography · 2004
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsnot available
FundersEurostarsInstitut Polaire Français Paul Emile VictorCentre National de la Recherche ScientifiquePolar Knowledge Canada
KeywordsGeologySeismogramSedimentCoringMineralogyDrillingSeismologyGeomorphologyMaterials science

Abstract

fetched live from OpenAlex

The depth‐scale accuracy of marine sedimentary series collected by coring is of key importance for the precise calculation of sedimentation rates and fluxes. For three giant piston cores collected during the InterPole MD99‐114/International Marine Past Global Changes Study (IMAGES) V cruise (MD99‐2227, MD99‐2246, and MD99‐2251), the 3.5‐kHz chirp profiles recorded on board are compared to synthetic seismograms computed from physical property logs measured on cores. In each case, the perfect match of main deep reflectors requires a significant upward shift of the water‐sediment (W/S) interface in the synthetic seismograms with respect to the 3.5‐kHz profiles. Since no drastic perturbation of the physical property logs is observed, this upward shift is interpreted as resulting from a significant sediment oversampling in the upper part of the cores. The affected depth intervals are consistent with the thickness of the perturbed zones observed in penetrometry and anisotropy of magnetic susceptibility records (∼10–15 m). To retrieve the true in situ sediment thickness, a linear depth correction is applied between consecutive acoustic reflectors to achieve a perfect match between the synthetic seismogram and the corresponding 3.5‐kHz profiles. Depth correction laws (amount of material excess as a function of initial depth) are deduced from this resynchronization procedure. First estimations of upper core oversampling rates range from 30% (core MD99‐2227) to 37% (core MD99‐2246). Moreover, we observe that some undersampling may also exist in the lower part of the sediment cores.

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)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.015
GPT teacher head0.228
Teacher spread0.213 · 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 designBench or experimental
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

Citations59
Published2004
Admission routes1
Has abstractyes

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