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Record W2803963591 · doi:10.3390/agriculture8060071

Determining the Stability of Sugarcane Filtercake Biochar in Soils with Contrasting Levels of Organic Matter

2018· article· en· W2803963591 on OpenAlexafffund
Alicia B. Speratti, Joan Romanyà, Jordi García-Pausas, Mark S. Johnson

Bibliographic record

VenueAgriculture · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCoal and Its By-products
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBiocharSoil waterOrganic matterAgronomyEnvironmental scienceSoil organic matterAgroforestryChemistryBiologySoil sciencePyrolysisEcology

Abstract

fetched live from OpenAlex

Sugarcane filtercake is a nutrient-rich residue produced prior to sugarcane distillation and is commonly disposed of by applying directly to agricultural fields, often causing high decomposition and leaching rates. Transforming this material into biochar could improve its stability in the soil. In this 92-day incubation study, filtercake biochar produced at 400 °C (BC400) and 600 °C (BC600) was used to trace biochar stability when mixed with two soils with different organic matter levels: an agricultural field (1.2% carbon (C)) and a forest (2.8% C) soil. Based on δ13C isotope analysis, biochar decreases in the field soil mostly occurred in the coarse silt fraction. In contrast, biochar decreases in forest soil appeared to be more equally distributed in all particle size fractions. A negative priming effect in biochar-amended soils was noticeable, mainly in the forest soil. Cumulative CO2 emissions were greater in soils with BC400 than in those with BC600 for both field and forest soils, while adding biochar increased CO2 emissions only in field soils. This increase did not appear to affect native soil organic matter pools. High-temperature filtercake biochar could thus be a more stable alternative to the current practice of raw filtercake applications.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.030
Threshold uncertainty score0.999

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)0.0020.000

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 head0.018
GPT teacher head0.188
Teacher spread0.170 · 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.

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

Citations11
Published2018
Admission routes2
Has abstractyes

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