Gas Exchanges of Yellow Capsicum Fertilized with Yellow Water and Cassava Wastewater
Bibliographic record
Abstract
Wastewater reuse has increasingly become a sustainable alternative to the efficient use of water as well as the mitigation of the negative environmental impacts generated by the release of the water to the environment in an indiscriminate way. Thus, it is very important to know and evaluate the physiological variables of gas exchanges of plants cultivated with these waters, in order to obtain precise answers their effects on plant physiology. The present study aimed to evaluate the gas exchange of yellow capsicum cultivated in soil fertilized with human urine and cassava wastewater. The experiment was conducted in a protected environment at the Federal University of Campina Grande, Campina Grande - PB. A completely randomized design with eight treatments and five replications was used. The treatments were cattle manure (T1); NPK (T2); human urine treated (T3); cassava wastewater (T4); (T3)+(T4); 2x(T3); 2x(T4); 2x(T3+T4). The volumes of the biofertilizers were defined according to the nitrogen and potassium contents of urine and cassava wastewater, respectively. At 15 and 30 days after transplanting, the variables of gas exchange, the efficient use of water and the instantaneous efficacy of carboxylation were evaluated. The data were submitted to Tukey test at 5% probability. The results indicated that there wasn’t statistical difference for efficient use of water (UEA) evaluated at 30 DAT.The treatment 5 provided the best results in relation to gas exchange, for the variables internal concentration of CO2 (Ci), net assimilation rates of CO2 (A), stomatal conductance (gs), leaf transpiration (E), higher efficiency of water use (UEA) and instantaneous carboxylation efficiency at 15 DAT, so for 30 DAT the biofertilizer that provided the best results was the T4 treatment, although there weren’t significant statistical differences among the treatments for (UEA), except for of T5.
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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 | 0.000 |
| 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".