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Record W3022007843 · doi:10.1073/pnas.2003276117

Evidence for sodium-rich alkaline water in the Tagish Lake parent body and implications for amino acid synthesis and racemization

2020· article· en· W3022007843 on OpenAlexafffund
L. F. White, K. T. Tait, Brian Langelier, Elizabeth A. Lymer, Ana Černok, T. V. Kizovski, Chi Ma, Oliver Tschauner, Richard I. Nicklin

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2020
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstro and Planetary Science
Canadian institutionsRoyal Ontario MuseumYork UniversityMcMaster UniversityUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Space AgencyMcMaster University
KeywordsRacemizationChemistrySodiumAqueous solutionAmino acidGeologyInorganic chemistryMineralogyStereochemistryOrganic chemistryBiochemistry

Abstract

fetched live from OpenAlex

Understanding the timing and mechanisms of amino acid synthesis and racemization on asteroidal parent bodies is key to demonstrating how amino acids evolved to be mostly left-handed in living organisms on Earth. It has been postulated that racemization can occur rapidly dependent on several factors, including the pH of the aqueous solution. Here, we conduct nanoscale geochemical analysis of a framboidal magnetite grain within the Tagish Lake carbonaceous chondrite to demonstrate that the interlocking crystal arrangement formed within a sodium-rich, alkaline fluid environment. Notably, we report on the discovery of Na-enriched subgrain boundaries and nanometer-scale Ca and Mg layers surrounding individual framboids. These interstitial coatings would yield a surface charge state of zero in more-alkaline fluids and prevent assimilation of the individual framboids into a single grain. This basic solution would support rapid synthesis and racemization rates on the order of years, suggesting that the low abundances of amino acids in Tagish Lake cannot be ascribed to fluid 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)0.001
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 score0.196
Threshold uncertainty score0.143

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 studies0.0000.000
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 head0.080
GPT teacher head0.314
Teacher spread0.234 · 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

Citations13
Published2020
Admission routes2
Has abstractyes

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