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Record W2609611632 · doi:10.1038/s41598-017-01612-8

Organic molecular heterogeneities can withstand diagenesis

2017· article· en· W2609611632 on OpenAlexfundno aff
Julien Alléon, Sylvain Bernard, Corentin Le Guillou, Damien Daval, Fériel Skouri‐Panet, M. Kuga, François Robert

Bibliographic record

VenueScientific Reports · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMetabolomics and Mass Spectrometry Studies
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchCentre National de la Recherche ScientifiqueAgence Nationale de la RechercheUniversity of SaskatchewanCanadian Light Source
KeywordsDiagenesisGeologyComputer sciencePaleontology

Abstract

fetched live from OpenAlex

Reconstructing the original biogeochemistry of organic fossils requires quantifying the extent of the chemical transformations that they underwent during burial-induced maturation processes. Here, we performed laboratory experiments on chemically different organic materials in order to simulate the thermal maturation processes that occur during diagenesis. Starting organic materials were microorganisms and organic aerosols. Scanning transmission X-ray microscopy (STXM) was used to collect X-ray absorption near edge spectroscopy (XANES) data of the organic residues. Results indicate that even after having been submitted to 250 °C and 250 bars for 100 days, the molecular signatures of microorganisms and aerosols remain different in terms of nitrogen-to-carbon atomic ratio and carbon and nitrogen speciation. These observations suggest that burial-induced thermal degradation processes may not completely obliterate the chemical and molecular signatures of organic molecules. In other words, the present study suggests that organic molecular heterogeneities can withstand diagenesis and be recognized in the fossil record.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.067
Threshold uncertainty score0.700

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 studies0.0010.000
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 head0.010
GPT teacher head0.244
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 designBench or experimental
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

Citations62
Published2017
Admission routes1
Has abstractyes

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