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Record W1943785559 · doi:10.1002/ggge.20054

Kimberlite eruptions as triggers for early Cenozoic hyperthermals

2013· article· en· W1943785559 on OpenAlexafffundabout
M. Patterson, D. Francis

Bibliographic record

VenueGeochemistry Geophysics Geosystems · 2013
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological and Geochemical Analysis
Canadian institutionsMcGill University
FundersMcGill University
KeywordsKimberliteGeologyCenozoicGeochemistryEarth scienceCarbon cycleMantle (geology)Carbon fibersPaleontologyStructural basin

Abstract

fetched live from OpenAlex

Abstract The early Cenozoic experienced at least three short but major hyperthermals associated with disruptions of the global carbon cycle. The largest among those, the Paleocene‐Eocene thermal maximum, was associated with a negative carbon isotopic excursion of ~ 2.5‰ that appears to be best explained by the thermal dissociation of methane hydrates due to an initial period of warming. The cause of the initial warming has been attributed to a massive injection of carbon (CO 2 and/or CH 4 ) into the atmosphere; however, the source of the carbon is as yet unknown. The emplacement of a large cluster of kimberlite pipes at ~56 Ma in the Lac de Gras region of northern Canada may have provided the carbon that triggered early warming in the form of exsolved magmatic CO 2 . Our calculations indicate that the estimated 900–1100 Pg of carbon required for the initial ~3°C of ocean water warming associated with the Paleocene‐Eocene thermal maximum could have been released during the emplacement of a large kimberlite cluster. The coeval ages of two other kimberlite clusters in the Lac de Gras field and two other early Cenozoic hyperthermals indicate that CO 2 degassing during kimberlite emplacement is a plausible source of the CO 2 responsible for these sudden global warming events.

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 categoriesMeta-epidemiology (narrow), Insufficient 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 score0.281
Threshold uncertainty score1.000

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)0.0150.005

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 head0.190
Teacher spread0.181 · 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

Citations16
Published2013
Admission routes3
Has abstractyes

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