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Record W2973224662 · doi:10.5539/jas.v11n16p38

Seeds of Carthamus Tinctorius Submitted to Hydration: Morphological Aspects and Emergence

2019· article· en· W2973224662 on OpenAlexvenueno aff
Claudia Borgmann, Luciene Kazue Tokura, Bruna de Villa, Deonir Secco, Jair Antônio Cruz Siqueira, Pablo Chang, Alessandra Mayumi Tokura Alovisi, Lucas Iarrocheski Rotta, Vinícius Miola, Leonardo Doreto da Silva, João Vitor Zanella, Milton Felipe Hurban Ramos dos Santos

Bibliographic record

VenueJournal of Agricultural Science · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSunflower and Safflower Cultivation
Canadian institutionsnot available
Fundersnot available
KeywordsCarthamusShootHorticultureBiologyDry weightBotanyMedicineTraditional medicine

Abstract

fetched live from OpenAlex

The present study aimed to evaluate the initial development of safflower genotypes (Carthamus tinctorius) after different periods of seed hydration. For the study two experiments were evaluated. At first, an experiment was performed with 0, 24, 48, 72, 168 and 360 hours of seed hydration, and in the second moment, another with 0, 12, 24, 36, 48 and 60 hours of hydration. The experimental design for the two experiments was completely randomized, with four replicates and six treatments. After 30 days of conduction of the experiments were analyzed the percentage of emergency, index of emergency speed, average time of emergency and average speed of emergency. The evaluated morphological characteristics were plant height, stem diameter, root length, fresh shoot and root mass and dry shoot mass. Hydration of seeds in considerable proportions positively influences the emergence and development of safflower plants. The highest performance in the emergence of plants, size, accumulation of fresh and dry shoot mass and fresh root mass was obtained by the IAPAR genotype, which stood out in relation to the other. The greatest emergencies were obtained with seeds with 36 hours of hydration, and the lowest emergence was found after 72 hours under hydration.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.931
Threshold uncertainty score0.311

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 science0.0000.000
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 head0.012
GPT teacher head0.216
Teacher spread0.204 · 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

Citations4
Published2019
Admission routes1
Has abstractyes

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