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Record W2610358958 · doi:10.1130/g39002.1

Middle Eocene CO<sub>2</sub>and climate reconstructed from the sediment fill of a subarctic kimberlite maar

2017· article· en· W2610358958 on OpenAlexafffundabout
Alexander P. Wolfe, Alberto V. Reyes, Dana L. Royer, David R. Greenwood, Gabriela Doria, Mary Gagen, Peter A. Siver, John A. Westgate

Bibliographic record

VenueGeology · 2017
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsUniversity of TorontoBrandon UniversityUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaNational Science Foundation
KeywordsSubarctic climatePaleoclimatologyMaarGeologyKimberlitePrecipitationSedimentTemperate climateClimate changeClimatologyPaleontologyOceanographyEcology

Abstract

fetched live from OpenAlex

Eocene paleoclimate reconstructions are rarely accompanied by parallel estimates of CO 2 from the same locality, complicating assessment of the equilibrium climate response to elevated CO 2 .We reconstruct temperature, precipitation, and CO 2 from latest middle Eocene (ca.38 Ma) terrestrial sediments in the posteruptive sediment fill of the Giraffe kimberlite in subarctic Canada.Mutual climatic range and oxygen isotope analyses of botanical fossils reveal a humidtemperate forest ecosystem with mean annual temperatures (MATs) more than 17 C warmer than present and mean annual precipitation ~4 present.Metasequoia stomatal indices and gas-exchange modeling produce median CO 2 concentrations of ~630 and ~430 ppm, respectively, with a combined median estimate of ~490 ppm.Reconstructed MATs are more than 6 C warmer than those produced by Eocene climate models forced at 560 ppm CO 2 .Estimates of regional climate sensitivity, expressed as MAT per CO 2 doubling above preindustrial levels, converge on a value of ~13 C, underscoring the capacity for exceptional polar amplification of warming and hydrological intensification under modest CO 2 concentrations once both fast and slow feedbacks become expressed.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.024
GPT teacher head0.238
Teacher spread0.214 · 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 teacher head, not a consensus.

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

Citations41
Published2017
Admission routes3
Has abstractyes

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