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Record W4388732856 · doi:10.1038/s41467-023-43129-x

Microbial methane cycling in a landfill on a decadal time scale

2023· article· en· W4388732856 on OpenAlexafffund
Daniel S. Grégoire, Nikhil A. George, Laura A. Hug

Bibliographic record

VenueNature Communications · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicLandfill Environmental Impact Studies
Canadian institutionsCarleton UniversityUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Waterloo
KeywordsMethaneEnvironmental scienceBiogeochemical cycleAnaerobic oxidation of methaneMunicipal solid wasteGreenhouse gasCyclingBiogasMethanogenesisMetagenomicsLandfill gasBiodegradable wasteEnvironmental chemistryWaste managementEcologyChemistryBiologyEngineering

Abstract

fetched live from OpenAlex

Landfills generate outsized environmental footprints due to microbial degradation of organic matter in municipal solid waste, which produces the potent greenhouse gas methane. With global solid waste production predicted to increase substantially in the next few decades, there is a pressing need to better understand the temporal dynamics of biogeochemical processes that control methane cycling in landfills. Here, we use metagenomic approaches to characterize microbial methane cycling in waste that was landfilled over 39 years. Our analyses indicate that newer waste supports more diverse communities with similar composition compared to older waste, which contains lower diversity and more varied communities. Older waste contains primarily autotrophic organisms with versatile redox metabolisms, whereas newer waste is dominated by anaerobic fermenters. Methane-producing microbes are more abundant, diverse, and metabolically versatile in new waste compared to old waste. Our findings indicate that predictive models for methane emission in landfills overlook methane oxidation in the absence of oxygen, as well as certain microbial lineages that can potentially contribute to methane sinks in diverse habitats.

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 categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.428
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.004

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 head0.015
GPT teacher head0.292
Teacher spread0.277 · 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.

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

Citations44
Published2023
Admission routes2
Has abstractyes

Explore more

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