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Record W2966139404 · doi:10.1126/science.aaw9247

The geologic history of seawater oxygen isotopes from marine iron oxides

2019· article· en· W2966139404 on OpenAlexaff
Nir Galili, Aldo Shemesh, Ruth Yam, Irena Brailovsky, Michal Sela-Adler, Elaine M. Schuster, Christopher J. Collom, Andrey Bekker, Noah J. Planavsky, Francis A. Macdonald, Alain Préat, Maxim Rudmin, Wiesław Trela, Ulf Sturesson, Jeffrey M. Heikoop, Marcos Aurell, Javier Ramajo, Itay Halevy

Bibliographic record

VenueScience · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicPaleontology and Stratigraphy of Fossils
Canadian institutionsRoyal Tyrrell Museum
FundersH2020 European Research CouncilRussian Science FoundationWeizmann Institute of ScienceRussian Foundation for Basic ResearchNational Science Foundation
KeywordsSeawaterIsotopes of oxygenIsotopeEnvironmental chemistryOxygenFractionationOxygen isotope ratio cycleGeologyStable isotope ratioComposition (language)OceanographyEarth scienceChemistryEnvironmental scienceGeochemistry

Abstract

fetched live from OpenAlex

Not as hot as we thought Earth's early oceans appear not to have been as hot as some have suggested. The oxygen isotope composition of marine carbonates has changed markedly over the past 3.5 billion years. However, it has been difficult to determine whether that is because of a cooling of the seawater (from temperatures as high as 70°C) or an actual change in the isotope composition of the water. Galili et al. calibrated the temperature-dependent oxygen isotope fractionation between iron oxides and aqueous solutions and constructed an oxygen isotope record in marine iron oxides covering the past 2 billion years. Their findings suggest that a change in the isotope composition of the water, rather than its cooling, underlies the observed geological trend. Science , this issue p. 469

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 categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.142
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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.011
GPT teacher head0.189
Teacher spread0.178 · 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

Citations124
Published2019
Admission routes1
Has abstractyes

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