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Record W2971627277 · doi:10.1130/g46418.1

Cenozoic carbonate burial along continental margins

2019· article· en· W2971627277 on OpenAlexaff
Robin van der Ploeg, Bernard P. Boudreau, Jack J. Middelburg, Appy Sluijs

Bibliographic record

VenueGeology · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsDalhousie University
Fundersnot available
KeywordsGeologyCarbonateCenozoicWeatheringContinental marginPaleontologyCarbonate rockCarbon cycleGlacial periodEarth scienceOceanographyGeochemistrySedimentary rockTectonicsStructural basin

Abstract

fetched live from OpenAlex

Abstract Marine carbonate burial represents the largest long-term carbon sink at Earth’s surface, occurring in both deep-sea (pelagic) environments and shallower waters along continental margins. The distribution of carbonate accumulation has varied over geological history and impacts the carbon cycle and ocean chemistry, but it remains difficult to quantitatively constrain. Here, we reconstruct Cenozoic carbonate burial along continental margins using a mass balance for global carbonate alkalinity, which integrates independent estimates for continental weathering and pelagic carbonate burial. Our results indicate that major changes in marginal carbonate burial were associated with important climate and sea-level change events, including the Eocene-Oligocene transition (ca. 34 Ma), the Oligocene-Miocene boundary Mi-1 glaciation (ca. 23 Ma), and the middle Miocene climate transition (ca. 14 Ma). In addition, we find that a major increase in continental weathering from ca. 10 Ma to the present may have driven a concomitant increase in pelagic carbonate burial. Together, our results show that changes in global climate, sea level, and continental weathering have all impacted carbonate burial over the Cenozoic, but the relative importance of these processes may have varied through time.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.071
Threshold uncertainty score0.989

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0210.012

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 head0.009
GPT teacher head0.215
Teacher spread0.206 · 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; both teacher heads agree on what is shown here.

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

Citations17
Published2019
Admission routes1
Has abstractyes

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