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Record W2064270046 · doi:10.1038/srep03602

Long-term intensive management increased carbon occluded in phytolith (PhytOC) in bamboo forest soils

2014· article· en· W2064270046 on OpenAlex

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.

Bibliographic record

VenueScientific Reports · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSilicon Effects in Agriculture
Canadian institutionsUniversity of Alberta
FundersNatural Science Foundation of Zhejiang ProvinceNational Natural Science Foundation of China
KeywordsPhytolithMulchBambooSoil waterCarbon sequestrationSasaEnvironmental scienceSoil scienceAgronomyBiologyBotanyEcologyCarbon dioxide

Abstract

fetched live from OpenAlex

Carbon (C) occluded in phytolith (PhytOC) is highly stable at millennium scale and its accumulation in soils can help increase long-term C sequestration. Here, we report that soil PhytOC storage significantly increased with increasing duration under intensive management (mulching and fertilization) in Lei bamboo (Phyllostachys praecox) plantations. The PhytOC storage in 0-40 cm soil layer in bamboo plantations increased by 217 Mg C ha(-1), 20 years after being converted from paddy fields. The PhytOC accumulated at 79 kg C ha(-1) yr(-1), a rate far exceeding the global mean long-term soil C accumulation rate of 24 kg C ha(-1) yr(-1) reported in the literature. Approximately 86% of the increased PhytOC came from the large amount of mulch applied. Our data clearly demonstrate the decadal scale management effect on PhytOC accumulation, suggesting that heavy mulching is a potential method for increasing long-term organic C storage in soils for mitigating global climate change.

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)0.002
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 score0.059
Threshold uncertainty score0.652

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 science0.0000.000
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 head0.216
Teacher spread0.207 · 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