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Record W2930655781 · doi:10.1139/cgj-2018-0147

Modelling microbial growth and biomass accumulation during methane oxidation in unsaturated soil

2019· article· en· W2930655781 on OpenAlexvenueno aff
Song Feng, Anthony Kwan Leung, Charles Wang Wai Ng, Wan Peng Tan

Bibliographic record

VenueCanadian Geotechnical Journal · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicLandfill Environmental Impact Studies
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsMethaneAnaerobic oxidation of methaneBiomass (ecology)Environmental scienceBacterial growthPermeability (electromagnetism)Environmental chemistrySoil waterSoil scienceChemistryLandfill gasLimitingEnvironmental engineeringAgronomyBacteriaGeology

Abstract

fetched live from OpenAlex

Microbial aerobic methane oxidation (MAMO) affects methane emissions through landfill covers by not only consuming methane, but also causing biomass accumulation associated with bacterial growth. Although the reduction of soil porosity by biomass accumulation has been well recognized, most existing models ignore this effect when estimating MAMO efficiency. The present study proposes a newly improved theoretical model that could consider the effects of both microbial growth and biomass accumulation on MAMO during coupled water–gas–heat reactive transport in unsaturated soil. Comprehensive batch incubation tests were performed to determine the input parameters required. Part of a set of published experimental data was used to validate the new model, while the remainder of the dataset was used to evaluate the model predictability of soil–microbe interaction (i.e., class B prediction). When ambient temperature is relatively high (30 °C), ignoring biomass accumulation would lead to an overestimation of MAMO efficiency by more than three times. As the biomass accumulated in soil pores, the water permeability, gas permeability, and gas diffusion in the unsaturated soil reduced, consequently limiting the supply of oxygen to the bacteria for MAMO to take place.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.686
Threshold uncertainty score0.967

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 head0.015
GPT teacher head0.221
Teacher spread0.205 · 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
Published2019
Admission routes1
Has abstractyes

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