Growing, Production and Quality of Thornless Cactus Irrigated With Dairies Effluent
Bibliographic record
Abstract
The environmental pollution coming from dairies industries are the most hazardous of the economy sector due to the great amount of garbage produced. This work aimed it evaluating the dairies effluent on the morphometric, productive, bromatological, and nutritional effects of the thornless cactus. One trial was carried out it the Water Reuse Experimental Station during the period from April to December 2015. The experiment was carried out in randomized blocks design, with five treatments and five replications, totaling 25 experimental units. The treatments were irrigation with water of well (T1), irrigation with 10% of annual dose plus water of well (T2), irrigation with 20% of annual dose plus water of well (T3), irrigation with 30% of annual dose plus water of well (T4), and irrigation with 40% of annual dose plus water of well (T5). The growth parameteres determinations parameteres (production and quality) of thornless cactus were achieved at 240 days after planting. The highest productivity (28.2 Mg ha-1) was achieved with the treatment T4. The nitrogen concentrations were significative to thornless cactus, whereas the treatment T4 increased mostly the Nitrogen content in the plants. Then, the T4 treatment predominated, since it enhanced the crop productivity about crude protein and Nitrogen contents.
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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 | 0.001 |
| 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".