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Record W2943152327 · doi:10.5539/jas.v11n6p383

Biochar as Phosphorus Conditioner in Substrate for Brazil Nut (Bertholletia excelsa Humb. & Bonpl.) Seedling Production in the Central Amazon

2019· article· en· W2943152327 on OpenAlexvenueno aff
João Batista Dias Damaceno, Ana Cecília Nina Lobato, Romildo Torres da Gama, Danielle Monteiro de Oliveira, Newton Paulo de Souza Falção

Bibliographic record

VenueJournal of Agricultural Science · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicGrowth and nutrition in plants
Canadian institutionsnot available
Fundersnot available
KeywordsBiocharSeedlingPhosphorusShootBrazil nutHorticultureHuman fertilizationBiologyDry weightAnimal scienceNutAgronomyBotanyChemistryFood sciencePyrolysis

Abstract

fetched live from OpenAlex

The aim of this study was to evaluate the interaction of biochar and phosphorus in substrate for seedling production of Brazil nut. A greenhouse experiment was carried out with the following treatments: five doses of biochar (0, 20, 40, 60 and 80 t ha-1) and effect of two levels of phosphorus (0 and 100 kg ha-1 of P2O5) with 4 replicates. The plant height, stem diameter and number of leaves were monthly evaluated. At the end of experiment (180 days), the plants were removed from the pots and were evaluated the dry weight of the seedlings (total, shoot and roots), absolute growth rate, height/diameter and shoot/root ratio, number of leaves, leaf gain and quality index of seedlings. There was a significant interaction between the biochar and phosphorus interaction. The elevation of the bioburden doses did not influence any variables with phosphorus. In the absence of phosphorus, the doses of up to 40 t ha-1 of biochar promoted the highest values of the analyzed variables. Higher doses of biochar (> 40 t ha-1) reduced the growth and seedlings quality, however, it was found that under phosphate fertilization, the seedlings were obtained better growth results.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.848
Threshold uncertainty score0.180

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.238
Teacher spread0.223 · 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 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

Citations3
Published2019
Admission routes1
Has abstractyes

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