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Steel slag and phosphate nutrition of corn inoculated with arbuscular mycorrhizal fungi

2019· article· en· W2943809776 on OpenAlexaff
Anita Bueno de Camargo Nunes, Orivaldo José Saggin Júnior, Eliane María Ribeiro da Silva, Flávio Araújo Pinto, Jessé Valentim dos Santos, Marco Aurélio Carbone Carneiro

Bibliographic record

VenuePesquisa Agropecuária Brasileira · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMycorrhizal Fungi and Plant Interactions
Canadian institutionsDiscovery Air (Canada)
FundersConselho Nacional de Desenvolvimento Científico e TecnológicoFundação de Amparo à Pesquisa do Estado de Minas GeraisArcelorMittal
KeywordsPhosphorusInoculationSlag (welding)PhosphateShootPhosphoriteHuman fertilizationHorticultureNutrientAgronomyDry matterChemistryBiologyMetallurgyMaterials science

Abstract

fetched live from OpenAlex

Abstract: The objective of this work was to evaluate the effect of the use of steel slag as a soil acidity corrective and of mycorrhizal fungi associated with phosphate fertilization on corn plants. The study was performed in a greenhouse, using 3-kg pots with a Typic Haplorthox, in a 4x5x2 factorial arrangement, with four acidity correction treatments (C1, correction with dolomitic limestone at a dose of 4 Mg ha-1; C2, correction with steel slag at a dose of 4 Mg ha-1; C3, correction with a 1:1 mixture of 2 Mg ha-1 dolomitic limestone and 2 Mg ha-1 steel slag; and C4, control, without pH correction), five phosphorus doses (0, 42, 95, 213, and 480 mg dm-3), and the presence or absence of two arbuscular mycorrhizal fungi (Rhizophagus clarus and Gigaspora margarita), with five replicates. Steel slag was efficient in correcting soil pH and providing Ca and Mg for the plants; therefore, it could replace limestone. The inoculation with R. clarus and G. margarita, associated with P doses of 42, 95, and 213 mg dm-3, improved the development of corn plants after 45 days, resulting in greater plant height, stem diameter, leaf area, and shoot and root dry matter.

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 score0.392
Threshold uncertainty score0.995

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)0.0060.001

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 head0.007
GPT teacher head0.186
Teacher spread0.179 · 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

Citations6
Published2019
Admission routes1
Has abstractyes

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