Postharvest Quality of Beetroots Grown Under Different Irrigation Depths and Ascorbic Acid Doses
Bibliographic record
Abstract
Beetroot (Beta vulgaris L.) is a culture of great demand in Brazil due to its high nutritional value. However, water availability is a determining factor on its production. An alternative to reduce the damage caused by water stress is to apply organic solutes, such as ascorbic acid. The purpose hereof was to evaluate the postharvest quality of beetroots grown under different irrigation depths and ascorbic acid doses. The experiment was carried out in the Human, Social, and Agricultural Center’s Postharvest Physiology and Technology Laboratory of the Federal University of Paraíba, Bananeiras, Paraíba, Brazil, in a completely randomized design with five doses of ascorbic acid (0.00, 0.29, 1.00, 1.71, and 2.00 mM) and five irrigation depths (40.0%, 51.6%, 80.0%, 108.4%, and 120.0% of the evapotranspiration), combined according to each Box Central Compound experimental matrix, totaling nine treatments with five repetitions. The variables total soluble solids, electrical conductivity, hydrogenation potential, titratable acidity, total soluble solids and titratable acidity ratio, moisture, dry matter, and mineral matter were evaluated. The data were submitted for analysis of variance and polynomial regression. There was a significant interaction between the irrigation depths and the ascorbic acid doses in every variable, except for electrical conductivity and mineral matter. The postharvest characteristics of beetroots improved with applications of ascorbic acid doses in the thinner irrigation depths. The greatest dose of ascorbic acid (2 mM) in the thinner irrigation depth (40%) increases the postharvest quality of beet tuberous roots.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".