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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 OpenAlex
Carlos Barreto, Zoë Lindo

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

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.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.040
Threshold uncertainty score0.343

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 head0.010
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