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Record W4255521727 · doi:10.5194/soil-2019-42

Identification of new microbial functional standards for soil quality assessment

2019· preprint· en· W4255521727 on OpenAlexaff
Sören Thiele‐Bruhn, Michael Schloter, Berndt‐Michael Wilke, Lee A. Beaudette, Fabrice Martin‐Laurent, Nathalie Cheviron, Christian Mougin, Jörg Römbke

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicMicrobial Community Ecology and Physiology
Canadian institutionsEnvironment and Climate Change Canada
FundersAnalyses et Expérimentations pour les EcosystèmesAgence Nationale de la Recherche
KeywordsEnvironmental scienceSoil qualityStandardizationSoil functionsNutrient cycleSoil biologySoil healthEcosystem servicesBiomass (ecology)Environmental resource managementSoil carbonSoil organic matterBiochemical engineeringEcosystemComputer scienceEcologySoil waterSoil biodiversityBiologyEngineeringSoil science

Abstract

fetched live from OpenAlex

Abstract. The activity of microorganisms in soil is important for a robust functioning soil and related ecosystem service. Hence, there is a necessity to identify the indigenous soil microbial community for its functional properties using soil microbiological methods in order to determine the natural properties, functioning and operating range of soil microbial communities, and to assess ecotoxicological effects due to anthropogenic activities. Numerous microbiological methods currently exist in the literature and new, more advanced methods continue to be developed; however, only a limited number of the methods are standardized. Consequently, there is a need to identify the most promising non-standardized methods for assessing soil quality and develop these into standards. In alignment with the "Ecosystem Service Approach", new methods should focus on soil microbial function, including nutrient cycling, pest control and plant growth promotion, carbon cycling and sequestration, greenhouse gas emission, and soil structure. The few existing, function-related standard methods available focus on the estimation of microbial biomass, basal respiration, enzyme activities related to nutrient cycling, and organic chemical biodegradation. This paper sets out to summarize and expand on recent discussions within the International Organization for Standardization (ISO), Soil Quality - Biological Characterization sub-committee (ISO TC 190/SC 4) where a need was identified to develop scientifically sound methods which would best fulfil the practical needs of future users for assessing soil quality. Of particular note was the current evolution of molecular methods in microbial ecology that uses quantitative real time PCR (qPCR) to produce a large number of new endpoints and is more sensitive as compared to "classical" methods. Quantitative PCR assesses the activity of microbial genes that code for enzymes that catalyse major transformation steps in nitrogen and phosphorus cycling, greenhouse gas emissions, chemical transformations including pesticide degradation, and plant growth promotion pathways. In the assessment of soil quality methods, it was found that fungal methods were significantly underrepresented. As such, techniques to analyse fungal enzyme activities are proposed. Additionally, methods for the determination of microbial growth rates and efficiencies, including the use of glomalin as a biochemical marker for soil aggregation, are discussed. Furthermore, field methods indicative of carbon turnover, including the litter bag test and a modification to the tea bag test, are presented. As a final note, it is suggested that endpoints should represent a potential function of soil microorganisms rather than actual activity levels, as the latter can largely be dependent on short-term variable soil properties such as pedoclimatic conditions, nutrient availability, and anthropogenic soil cultivation activities.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.013
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.013
Threshold uncertainty score0.069

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.003
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.051
GPT teacher head0.346
Teacher spread0.295 · 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 source (direct Gemma or distilled Codex), 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

Citations6
Published2019
Admission routes1
Has abstractyes

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