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Role of aromatic and non-protonated aromatic carbon in the stability of soil organic matter

2024· article· en· W4404437961 on OpenAlexafffund
Moazame Mesgar, Seyedahmad Kia, Paul Voroney, Andy Lo, Adam Gillespie

Bibliographic record

VenueOrganic Geochemistry · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoil Carbon and Nitrogen Dynamics
Canadian institutionsUniversity of Guelph
FundersOntario Ministry of Agriculture, Food and Rural Affairs
KeywordsEnvironmental chemistryOrganic matterTotal organic carbonSoil carbonCarbon fibersSoil organic matterEnvironmental scienceProtonationAromaticityChemistrySoil waterSoil scienceOrganic chemistryMaterials science

Abstract

fetched live from OpenAlex

Research on soil organic matter (SOM) in its natural state is vital for comprehending the mechanisms governing soil stability and carbon cycling, crucial in addressing global climate change. We utilized solid-state 13 C nuclear magnetic resonance (NMR) spectroscopy on HF treated samples, along with a 260-day laboratory mineralization experiment and thermal analysis-programmed pyrolysis (PP) to evaluate SOM biodegradability and thermal stability. To cover the potential range of organic carbon variability, we selected samples from three land uses and soil types, featuring total organic carbon levels from 1% to 39%. Our analysis confirmed the substantial contribution of non-protonated aromatic-carbon not bonded to oxygen to SOM’s biological and thermal stability, constituting approximately 14–21% of soil organic carbon. These components exhibited a strong correlation with SOM stability matrices, such as thermal stability and oxygen index determined by PP. Samples with a higher prevalence of these components also displayed the lowest cumulative carbon mineralization. These findings enhance our understanding of the stable SOM pool, aiding in the identification of sustainable soil management practices to mitigate climate change impacts on soil health and carbon dynamics.

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 score0.172
Threshold uncertainty score0.519

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 head0.005
GPT teacher head0.184
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 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

Citations6
Published2024
Admission routes2
Has abstractyes

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