Production of Seedlings of Yellow Passion Fruit Plant in Different Substrates and Saline Levels
Bibliographic record
Abstract
In the process of production of seedlings the formulation of substrates that supply the nutritional needs and make available appropriate humidity tenor is relevant for the success and the reduction of costs of the producing with other inputs. In this perspective, it is known that alternative substrates are rich in organic matter, which possesses extenuating action for the harmful effect of the salinity. Therefore, it was aimed at to evaluate the effect of saline waters and formulations of substrates in the production of seedlings of yellow passion fruit plant. Experiment was driven in atmosphere protected in the dependences of the Federal University of Campina Grande, Pombal, Paraíba. The randomized block design was used in factorial outline 5 × 5, being five levels of salinity of the irrigation water (0.3; 1.3; 2.3; 3.3 and 4.3 dS m-1) and five substrates: S1 = soil; S2 = soil, goat manure and bovine manure (1:1:1); S3 = soil and goat manure (3:1); S4 = soil and bovine manure (3:1); S5 = soil, goat manure and bovine manure (3:0.5:0.5). To the 52 days after the sowing the growth analyses and quality of the seedlings were checked. The growth of the passion fruit plant seedlings was inhibited by the salinity in the irrigation water. However, the use of organic inputs in the formulation of the substratum lessens the harmful effect of the saline stress, being the best substratum, soil and bovid manure in the proportion 3:1.
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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".