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Record W2043110792 · doi:10.1029/2003pa000927

Compound‐specific radiocarbon dating of the varved Holocene sedimentary record of Saanich Inlet, Canada

2004· article· en· W2043110792 on OpenAlexfundaboutno aff
Rienk H. Smittenberg, Ellen C. Hopmans, Stefan Schouten, J. M. Hayes, Timothy I. Eglinton, Jaap S. Sinninghe Damsté

Bibliographic record

VenuePaleoceanography · 2004
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsnot available
FundersDivision of Ocean SciencesNederlandse Organisatie voor Wetenschappelijk OnderzoekUniversity of VictoriaNational Science Foundation
KeywordsVarveRadiocarbon datingCrenarchaeotaHoloceneGeologyUpwellingOceanographySedimentary rockBiomarkerPhysical geographyPaleontologySedimentGeography

Abstract

fetched live from OpenAlex

The radiocarbon contents of various biomarkers extracted from the varve‐counted sediments of Saanich Inlet, Canada, were determined to assess their applicability for dating purposes. Calibrated ages obtained from the marine planktonic archaeal biomarker crenarchaeol compared favorably with varve‐count ages. The same conclusion could be drawn for a more general archaeal biomarker (GDGT‐0), although this biomarker proved to be less reliable due to its less‐specific origin. The results also lend support to earlier indications that marine crenarchaeota use dissolved inorganic carbon (DIC) as their carbon source. The average reservoir age offset ΔR of 430 years, determined using the crenarchaeol radiocarbon ages, varied by ±110 years. This may be caused by natural variations in ocean‐atmosphere mixing or upwelling at the NE Pacific coast but variability may also be due to an inconsistency in the marine calibration curve when used at sites with high reservoir ages.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.080

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
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 head0.013
GPT teacher head0.196
Teacher spread0.183 · 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

Citations98
Published2004
Admission routes2
Has abstractyes

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