Comparison of Fish Pond Waste Water with Manures under Garden Egg in Nigeria
Bibliographic record
Abstract
Two field experiments were conducted from 2012 to 2013 cropping seasons to evaluate the efficacy and also determine the optimum rates of application for fish pond waste water in comparison with three other manures including one inorganic fertilizer and two organic manures under garden egg (Solanum spp.) crop.Garden egg was fertilized with pond waste water (PW), poultry manure (PM), pig manure (PG) and NPK15:15:15 at equivalent nitrogen (N) rates (0, 150, 300, 450 kg/ha). The treatments were arranged in a split plot under randomized complete block design (RCBD).The results obtained indicated a slight drop in soil pH but soil OM, total N, available P, exchangeable Ca and K increased generally with increasing rates of manures. The PW and PG treatments supported significantly (P<0.05) the highest yields of the crop compared to PM and NPK with the 150 and 300 kg/ha treatments recording the highest increase. These results have shown that under the high rainfall and acidic soil conditions of Akwa Ibom State in Nigeria, PW is useful for the improvement of soil conditions and achievement of higher crop yields when applied at rates that supply N in the range of 150 – 300 kg/ha.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".