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Record W4390511984 · doi:10.1038/s41467-023-44431-4

Universal microbial reworking of dissolved organic matter along environmental gradients

2024· article· en· W4390511984 on OpenAlexafffund
Erika C. Freeman, Erik J. S. Emilson, Thorsten Dittmar, Lucas P. P. Braga, Caroline E. Emilson, Tobias Goldhammer, Christine Martineau, Gabriel Singer, Andrew J. Tanentzap

Bibliographic record

VenueNature Communications · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicMicrobial Community Ecology and Physiology
Canadian institutionsTrent UniversityNatural Resources CanadaCanadian Forest Service
FundersCanadian Forest ServiceHorizon 2020 Framework ProgrammeGates Cambridge TrustNatural Resources CanadaU.S. Forest ServiceGovernment of Canada
KeywordsDissolved organic carbonEnvironmental chemistryEnvironmental scienceSoil waterSoil carbonEcosystemOrganic matterSoil organic matterCarbon cycleTotal organic carbonSoil scienceEcologyChemistryBiology

Abstract

fetched live from OpenAlex

Soils are losing increasing amounts of carbon annually to freshwaters as dissolved organic matter (DOM), which, if degraded, can offset their carbon sink capacity. However, the processes underlying DOM degradation across environments are poorly understood. Here we show DOM changes similarly along soil-aquatic gradients irrespective of environmental differences. Using ultrahigh-resolution mass spectrometry, we track DOM along soil depths and hillslope positions in forest catchments and relate its composition to soil microbiomes and physico-chemical conditions. Along depths and hillslopes, we find carbohydrate-like and unsaturated hydrocarbon-like compounds increase in abundance-weighted mass, and the expression of genes essential for degrading plant-derived carbohydrates explains >50% of the variation in abundance of these compounds. These results suggest that microbes transform plant-derived compounds, leaving DOM to become increasingly dominated by the same (i.e., universal), difficult-to-degrade compounds as degradation proceeds. By synthesising data from the land-to-ocean continuum, we suggest these processes generalise across ecosystems and spatiotemporal scales. Such general degradation patterns can help predict DOM composition and reactivity along environmental gradients to inform management of soil-to-stream carbon losses.

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 categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.810
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 science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.008
GPT teacher head0.233
Teacher spread0.226 · 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; both teacher heads agree on what is shown here.

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

Citations95
Published2024
Admission routes2
Has abstractyes

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