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Record W2132555540 · doi:10.1093/petrology/egm057

Contrasting Cooling Rates in the Lower Oceanic Crust at Fast- and Slow-spreading Ridges Revealed by Geospeedometry

2007· article· en· W2132555540 on OpenAlexaff
L. A. Coogan, Gawen R. T. Jenkin, Robert N. Wilson

Bibliographic record

VenueJournal of Petrology · 2007
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological and Geochemical Analysis
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsGeologyOlivineOceanic crustOphioliteMid-ocean ridgeMantle (geology)Seafloor spreadingCrustLithosphereGeochemistryPetrologyGeophysicsSubductionSeismologyTectonics

Abstract

fetched live from OpenAlex

Two approaches to determining the high-temperature (10008C to 6008C) cooling rate of the lower oceanic crust and upper mantle are presented and critically evaluated.The first is based on the down-temperature diffusive exchange of Ca between olivine and clinopyrox-ene. The second, less well-constrained, approach is based on the down-temperature diffusive exchange of Mg and Fe between olivine and spinel. Cooling rates based on olivine^spinel geospeedometry are approximately an order of magnitude faster than those from Ca-in-olivine geospeedometry. In contrast, cooling rates derived from thermochronology and remanent magnetism are approximately an order of magnitude slower than those derived by Ca-in-olivine geospeedometry; this is probably because they record cooling at lower temperatures. Using the Ca-in-olivine geospeedometer, the cooling rate of samples from the lower oceanic crust and upper oceanic mantle formed in the Oman ophiolite and in the three main ocean

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.009
GPT teacher head0.219
Teacher spread0.210 · 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
Published2007
Admission routes1
Has abstractyes

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