Growth and Production of Cultivars Ornamental Sunflower Irrigated With Water of Different Salinities
Bibliographic record
Abstract
The sunflower (Helianthus annuus L.) appears as an income alternative to the producers of the northeastern region of Brazil, however limited by the salinity present in the waters of the region, which makes it necessary to select cultivars that are more adaptable to this situation. On this, the objective was assessing the development of two cultivars of cutting ornamental sunflower with waters of different salinities. The experiment was performed in conditions of protected environment at UFCG, CCTA-Pombal-PB. It was performed in outline of blocks in factorial scheme 5 × 2, being five saline levels (N1 = 0.3, supply water; N2 = 1.5; N3 = 2.7; N4 = 3.9; and N5 = 5.1 dS m-1) and two cutting ornamental sunflower cultivars (Red Sun and Vincents II), with four repetitions and two plants by portion, with a total of 80 experimental units. The irrigation water salinity affected all the analyzed variables, up to estimate level of 2.7 dS m-1 acceptable reductions happen of 10% in the inflorescence and stem of cultivars of ornamental sunflower. The Red Sun cultivar when compared with cultivar Vincents II demonstrated better results in the studied variables.
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 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) | 0.000 | 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".