Behavior of Sunflower Irrigated With Salt Water and Nitrogen Fertigation
Bibliographic record
Abstract
The sunflower (Helianthus annus L.) crop has been gaining prominence in wastewater research due to its relevance to the production of edible oil, biodiesel and ornamental and decorative material. In the field of floriculture, the crop has been emerging as an alternative offer of a short-cycle product, with contrasting colors and easy identification by the consumer. The objective of this work was to evaluate the different electrical conductivities of water associated with nitrogen fertigation rates on the vegetative, physiological and productive behavior of ornamental sunflower. The experimental design was completely randomized, in a factorial scheme 6x4, referring to six levels of electrical conductivity of water (ECa of 0.5, 1.5, 2.5, 3.5, 4.5 and 5.5 dS m-1) and four nitrogen fertigation rates, with one control and the other three applications performed every 10, 20 and 30 days after emergence (DAE), with five replications, totaling 120 experimental units. The variables analyzed were: stem diameter, root length, chlorophyll a, b and total chlorophyll content, fresh and dry phytomass of shoots, roots and total phytomass, seed mass and capitulum mass. The salinity negatively affected the physiological variables, growth and productivity evaluated, decreasing with increasing electrical conductivity of irrigation water (dS m-1). Nitrogen fertigation at 10 days after emergence resulted in an increase in the stem diameter of sunflower plants.
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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) | 0.000 | 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 | 0.000 |
| 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".