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Record W2225110666

Understanding the Middle Miocene Climatic Optimum: Evaluation of Deuterium Values (δD) Related to Precipitation and Temperature

2013· article· en· W2225110666 on OpenAlexaboutno aff
Colin S Gannon

Bibliographic record

VenueBryant Digital Repository (Bryant University) · 2013
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsnot available
FundersChinese Academy of SciencesFranklin and Marshall CollegeRhode Island Space Grant ConsortiumYale UniversityNational Aeronautics and Space Administration
KeywordsPrecipitationGeologyDeuteriumClimatologyEnvironmental scienceMeteorologyGeography
DOInot available

Abstract

fetched live from OpenAlex

The Middle Miocene Climate Optimum was a unique warming period in the Earth’s geologic history, when a high global mean annual temperature was accompanied by a relatively low global CO2 concentration. Hydrogen isotopic signals (specifically molecular δD, the ratio of deuterium to hydrogen) from lipids of fossils and sediments offer intrinsic insights into precipitation of ancient climates. Using samples collected from known Middle Miocene deposits, we measured δD of n-alkanes extracted from well-preserved plant and sediment samples from varying latitudes across the Northern Hemisphere, and then analyzed the data through a zonally averaged precipitation and evaporation climate model. The reduced latitudinal temperature gradient with warm polar regions during the Middle Miocene was also contrarily coupled with a small variance in latitudinal meteoric water composition and precipitation. With our latitudinally variant sample locations (ranging from 24°N in Xianfeng, China, to 74°N in Banks Island, Canada), we developed a one-dimensional model in which we assessed evaporation and precipitation gradients throughout the Northern Hemisphere. Ultimately, we used the latitudinal distribution of δD to better constrain the atmospheric conditions during the Middle Miocene Climatic Optimum.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.404

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 communication0.0000.001
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 head0.057
GPT teacher head0.224
Teacher spread0.167 · 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.

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

Citations0
Published2013
Admission routes1
Has abstractyes

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