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Record W3006009512 · doi:10.5539/jas.v12n3p222

Biochar Yield From Shell of Brazil Nut Fruit and Its Effects on Soil Acidity and Phosphorus Availability in Central Amazonian Yellow Oxisol

2020· article· en· W3006009512 on OpenAlexvenueno aff
Dener Márcio da Silva Oliveira, Newton Paulo de Souza Falção, João Batista Dias Damaceno, Iraê Amaral Guerrini

Bibliographic record

VenueJournal of Agricultural Science · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicGrowth and nutrition in plants
Canadian institutionsnot available
Fundersnot available
KeywordsBiocharOxisolPhosphorusChemistryAgronomySoil fertilityEnvironmental scienceRaw materialSoil pHPyrolysisSoil waterSoil scienceBiology

Abstract

fetched live from OpenAlex

Phosphorus is one of the most limiting elements in the amazon soil, requiring low cost alternatives that increase the agronomic efficiency of phosphate fertilizers for satisfactory crop production and biochar has been used as an option for increase soil fertility. The objective of this work was to evaluate the yield and properties of the biochar produced from the shell of Brazil nut fruit at 500 oC, as well as its behavior in the acidity and phosphorus availability from mineral source in Yellow Oxisol. The experimental design was completely randomized in a factorial arrangement (5 × 5) with five doses of biochar (0, 20, 40, 60 and 80 t ha-1) and five doses of P2O5 (0, 100, 200, 300 and 400 kg ha-1) in 20 kg pots. The trial was carried out at 365 days and the yield and properties of the produced biochar were evaluated, as well as the determination of acidity and total and available phosphorus. The biochar produced from feedstock was considered satisfactory, with 59%, which is a good alternative for producers. Aluminum contents were reduced confirming the potential of biochar as a corrective for acidity. Additionally, the amount of total and available phosphorus increased with increasing biochar doses. Thus, not only the feedstock but also the pyrolysis temperature showed hight potencial to improve the amount of phosphorus in the soil and decrease the soil acidity.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.657
Threshold uncertainty score0.208

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.015
GPT teacher head0.204
Teacher spread0.189 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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
Published2020
Admission routes1
Has abstractyes

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