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Record W2823343103 · doi:10.5539/jas.v10n8p305

Chamomile Cultivation Submitted to Ultra-diluted Phosphorus Solutions

2018· article· en· W2823343103 on OpenAlexvenueno aff
Cassiane Ubessi, Valéria Dos Santos Da Rosa, Dionatan Ketzer Krysczun, Fernanda Carini, Solange Bósio Tedesco, Cristiane de Bona da Silva, Jerônimo Luiz Andriolo

Bibliographic record

VenueJournal of Agricultural Science · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBanana Cultivation and Research
Canadian institutionsnot available
Fundersnot available
KeywordsInflorescenceHectarePhosphorusCultivarHorticultureDistilled waterBiologyBotanyChemistry

Abstract

fetched live from OpenAlex

Homeopathic medicines may present differentiated responses in the cultivation of medicinal plants, altering the plant metabolism. The aim was to verify and describe the pathogenic symptoms caused by the application of Phosphorus in the cultivation of chamomile, aiming at the production of flowers. Four dynamizations, 3CH, 6CH, 12CH and 30CH (centesimal scale: CH = centesimal hahnemannian), were evaluated in the Mandirituba chamomile cultivar. Control pots received only distilled water and 70% alcohol in the same amount and proportion of homeopathic medicine. The variables analyzed from the harvest of the flowers are related to the parameters of inflorescence and plant production of chamomile. There was no difference between the dynamizations applied for the number of flowers plant-1, dry mass plant-1, dry mass hectare-1, number of branches and plant height. The characters of flowers mass plant-1, and production of fresh and dried flowers did not differ from the averages presented by the witnesses; however, in the 30CH dynamization, values higher than the others were observed, as well as for fresh mass plant-1 and hectare-1. The diameter and the height of the flower expressed better results in the 3CH dynamization. The application of Phosphorus promotes pathogenesis in the cultivation of chamomile with increases in the characters related to the inflorescence.

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)0.001
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 score0.857
Threshold uncertainty score0.711

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0010.000
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 head0.037
GPT teacher head0.270
Teacher spread0.234 · 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

Citations10
Published2018
Admission routes1
Has abstractyes

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