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Record W3005099342 · doi:10.1002/essoar.10500673.1

The Effect of Pressure on the Prebiotic Carbon of the Early Solar System

2019· article· en· W3005099342 on OpenAlexaff
Wren Montgomery, John S. Tse, Ketao Yin, Mark A. Sephton

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicOrigins and Evolution of Life
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsDeoxyriboseFourier transform infrared spectroscopyChemistryRiboseAstrobiologyPrebioticSolar SystemHigh pressureCarbon fibersInfraredSynchrotronChemical engineeringNucleic acidOrganic chemistryMaterials scienceThermodynamicsPhysicsBiochemistryOptics

Abstract

fetched live from OpenAlex

High pressures, along with thermal processes and irradiation, have a measurable effect on carbonaceous compounds which are found throughout the solar system. High pressure environments, such as those generated by impacts, occur frequently during the evolution of the solar system, and the effects of pressure on the carbonaceous materials present can influence the subsequent chemistry of the body. In situ high pressure synchrotron source Fourier Transform Infrared (FTIR) spectroscopy coupled with computational models has been used to directly study the effects of pressure on carbonaceous materials. Our work using these techniques investigates the specific case of structural sugars, where ribose and deoxyribose have differing responses to high pressures. These particular carbonaceous materials play a key role in the prebiotic chemistry of the early Earth as constituents of the bioinformational molecules ribonucleic acid (RNA) and deoxyribonucleic acid (DNA) respectively. Ribose is substantially less stable than deoxyribose at pressures exceeding 14 GPa and shows less recovery on decompression. Our results imply that the modest impacts experienced throughout the solar system could substantially alter the carbonaceous payload of many bodies, with consequences for any prebiotic chemistry.

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 score0.417
Threshold uncertainty score0.103

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)0.0000.000

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 head0.003
GPT teacher head0.182
Teacher spread0.179 · 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
Published2019
Admission routes1
Has abstractyes

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