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Boron nutrition and yield of alfalfa cultivar crioula in relation to boron supply

2004· article· en· W2170117505 on OpenAlexaff
Anacleto Ranulfo dos Santos, Waldssimiler Teixeira de Mattos, Ana Aparecida da Silva Almeida, Francisco Antônio Monteiro, Beatriz Dias Corrêa, Umesh Gupta

Bibliographic record

VenueScientia Agricola · 2004
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Micronutrient Interactions and Effects
Canadian institutionsAgriculture and Agri-Food Canada
FundersConselho Nacional de Desenvolvimento Científico e TecnológicoCoordenação de Aperfeiçoamento de Pessoal de Nível SuperiorFundação de Amparo à Pesquisa do Estado de São Paulo
KeywordsBoronCultivarChemistryMedicago sativaYield (engineering)Dry matterHorticultureGreenhousePlant growthAgronomyBotanyBiologyMaterials science

Abstract

fetched live from OpenAlex

Alfalfa cultivar Crioula (Medicago sativa cv. Crioula) is grown in South Brazil and only a few studies on the plants' boron requirement are available. A greenhouse experiment was carried out with alfalfa to measure boron acquisition, production and distribution in the plant; data on critical level and production potentials were recorded. Plants were grown in ground quartz added with 1 L of solution, with the following boron rates: 0, 0.0625, 0.125, 0.25, 0.50, 1.00, and 2.00 mg L-1. Plants were harvested at 46 days of growth. Forage dry mass was increased by boron supply and dry matter accumulation was considerably low in control. Boron concentration in the leaves was higher than in the stems or roots. Boron utilization from the external solution reached 90% at 0.0625 mg L-1 and sharply decreased with further increasing boron rates. Boron concentration and content in the leaves and in plant tops were at maximum when applied boron was between 1.5 and 1.6 mg L-1. Critical levels of boron in plant were 61 mg kg-1 in the leaves and 39 mg kg-1 in plant tops for this cultivar of alfalfa.

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 score0.904
Threshold uncertainty score0.183

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 head0.011
GPT teacher head0.217
Teacher spread0.206 · 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

Citations21
Published2004
Admission routes1
Has abstractyes

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