Study on Shrimp Waste Water and Vermicompost as a Nutrient Source for Bell Peppers
Bibliographic record
Abstract
The aquaculture industry generates significant nutrient-rich wastewater that is released into streams and rivers causing environmental concern. The objective of this controlled environment study was to evaluate the effect of waste shrimp water (SW), vermicompost (VC), at rates of 10%, 20%, 40%, and 80% by volume alone or in combination with SW, controlled-release fertilizer (CRF), and water-soluble fertilizer (WSF) on bell peppers ( Capsicum annuum L.) cv. X3R Red Knight. Application of VC at 80% or SW alone increased yields relative to unfertilized control. Combined applications of VC and SW increased yields compared with VC alone. Overall, total yields were greatest in the chemical fertilizer treatments (CRF and WSF) and least in the unfertilized control. SW and VC increased growth medium pH relative to the unfertilized control or to the chemical fertilizer treatments. In pepper fruits, the greatest nitrogen (N) content was found in the CRF treatment, although it was not different from VC at high rates or WSF treatments. Phosphorus concentration in peppers was greatest in the CRF treatment, less in all VC or SW treatments, but not different from unfertilized control or WSF treatment. Iron, magnesium (Mg), and zinc concentrations in peppers were greatest in CRF treatment but not different from control or WSF treatments. Overall, N accumulation in peppers was negatively correlated to growth medium pH and calcium (Ca); phosphorus (P) in peppers was negatively correlated to growth medium pH, Ca, and sodium (Na), whereas potassium (K) in peppers was negatively correlated to growth medium P, Mg, and Na. Results indicated: 1) SW may not be a viable pepper nutrient source; (2) SW can provide a similar nutrient supply as VC; and (3) chemical fertilizers can provide higher pepper yields compared with SW or VC alone or in combination.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".