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Record W3081317329 · doi:10.3389/frym.2020.00107

Decomposition in Peatlands: Who Are the Players and What Affects Them?

2020· article· en· W3081317329 on OpenAlexafffundabout
Carlos Barreto, Zoë Lindo

Bibliographic record

VenueFrontiers for Young Minds · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicPeatlands and Wetlands Ecology
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of CanadaOntario Ministry of Research, Innovation and ScienceOntario Ministry of Natural Resources and ForestryMinistry of Natural Resources
KeywordsPeatEnvironmental scienceCarbon fibersDecompositionSoil carbonEcologySoil waterSoil scienceBiologyMathematics

Abstract

fetched live from OpenAlex

All soils store carbon. As plants grow, they take up carbon from the atmosphere and this carbon enters the soil when they die. This dead plant material slowly decomposes as organisms, such as bacteria, fungi, and tiny animals called mites and springtails use this carbon as a food source. Decomposition is very slow in peatlands, and as a result, much of the carbon from dead plants remains in the soil, which can help slow climate warming. Decomposition in peatlands depends on how wet the soil is, and the different types of plants and soil organisms. We discovered that, in a peatland in northern Canada, dead plant material of different plant types decomposed at different rates, and more mites and springtails aiding in decomposition were found in wetter areas. Because peatlands are important for carbon storage, understanding who the players of decomposition are is important for understanding how to slow climate warming.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.071
Threshold uncertainty score0.141

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0030.002
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.010
GPT teacher head0.219
Teacher spread0.209 · 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 designTheoretical or conceptual
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

Citations10
Published2020
Admission routes3
Has abstractyes

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