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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".