Use of fishpond sediment for sustainable aquaculture—agriculture farming
Bibliographic record
Abstract
An experiment was carried out in triplicate, in 1 1 m 2 plots using six treatments, viz. zero input control (S 0 N 0 P 0 K 0 ), fertilizer control (S 0 NPK), sediment 60 kg without fertilizer (S 60 N 0 P 0 K 0 ), sediment 60 kg with N and K (S 60 NP 0 K), sediment 120 kg without fertilizer (S 120 N 0 P 0 K 0 ) and sediment 120 kg with N and K (S 120 NP 0 K) to determine the potential of tilapia pond sediment to supply P to morning glory, and the effects on the soil aggregate stability and the bulk density. The application of 60 and 120 kg sediment plot -1 corresponds to 30% and 60% of the plot soil by weight, respectively. The study confirmed that the application of tilapia pond sediment at 30% to farm soils with supplementation of N and K, i.e. the treatment S 60 NP 0 K, provided the required amount of P to morning glory and gave fresh and dry matter yields of morning glory equal to the fertilizer control plot. Furthermore, the application of 30% sediment significantly improved the soil aggregate stability and decreased the bulk density of farm soils to favorable levels. This kind of integration would ensure long-term sustainability of both aquaculture and agriculture farming.
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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 | 0.001 |
| 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".