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Nutrient uptake by grape in a Brazilian soil affected by rock biofertilizer

2011· article· en· W2141779167 on OpenAlexaff
Newton Pereira Stamford, Izabelle Pereira Andrade, Junior M.A. Lira, C. E. R. S. Santos, Ana Dolores Santiago de Freitas, Peter van Straaten

Bibliographic record

VenueJournal of soil science and plant nutrition · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicArsenic contamination and mitigation
Canadian institutionsUniversity of Guelph
FundersBanco do Nordeste do BrasilFundação de Amparo à Ciência e Tecnologia do Estado de PernambucoConselho Nacional de Desenvolvimento Científico e Tecnológico
KeywordsBiofertilizerFertilizerPotashInoculationPhosphoriteEarthwormHorticultureNutrientChemistrySulfurAgronomyBiology

Abstract

fetched live from OpenAlex

PK rock biofertilizers made from rocks and elemental sulphur inoculated with Acidithiobacillus improve yield of many short cycle plants similarly to soluble fertilizers.This study aims to evaluate the potential of PK rock biofertilizers for grape cultivation in the Brazilian San Francisco Valley.Three sources of P and K were compared: (a) soluble fertilizers, (b) biofertilizers plus elemental sulphur inoculated with Acidithiobacillus, and (c) ground phosphate and potash rocks, all at three application rates.A control treatment without P and K fertilization was added.Earthworm compound was applied as N source in all treatments.Grape (Vitis vinifera cv.Italia Pirovano) was cultivated in a dystrophic Planossol (medium texture) at the San Francisco River in the Brazilian Semiarid.P, K, Ca, Mg, S-SO 4 2-and Fe concentrations were analyzed in grape leaves and fruits.The results showed adequate leaf contents of S-SO 4 2-, K, and Fe with PK biofertilizer application plus earthworm compound, which indicates this may be alternative to soluble fertilizer for grape in soils with low available P and K.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.038
Threshold uncertainty score0.076

Distilled classifier scores by category (both heads)

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 head0.009
GPT teacher head0.201
Teacher spread0.191 · 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 source (direct Gemma or distilled Codex), 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

Citations17
Published2011
Admission routes1
Has abstractyes

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