Comparison of Physio-Chemical Characteristics of Different Compost Samples
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Bibliographic record
Abstract
The objective of the current study was to compare and analyze physiochemical characteristics of compost samples and determination of solid waste being dumped in a landfill site in Lahore, Pakistan. Different compost samples were analyzed to evaluate their physiochemical characteristics. The samples tested were collected from three different sources, i.e., Waste Buster, Kinnaird College, and Lahore Compost Private Limited, and compared with the waste sample dumped at Mahmood Booti landfill site. The analysis showed that the percentage composition of organics was highest than the other components in all the samples. The parameters that were analyzed include pH, moisture content, bulk density, salinity, carbon-nitrogen ratio, sodicity, available carbon, burned carbon, potassium, phosphorous, nitrogen, pathogens, gravel, and stones. The results were compared to the permissible limits according to The Pakistan Environmental Protection Agency (EPA) guidelines. Most of the sample components were under the permissible limits, whereas a few others were not, such as potassium and burned carbon. The amount of potassium was found to be 0.60 mg/L, 0.61 mg/L, and 0.61 mg/L for the samples collected from Waste Buster, Kinnaird College, and Lahore Compost Private Limited , respectively. This is much less than the standards set by the EPA i.e., 620-2280 mg/L which can lead to deficiency of nutrients in the compost. Burned carbon was found to be 46%, and 41% in the samples from Waste Buster and Kinnaird College respectively, which is higher than the standard of 35%. The higher amount of burned carbon can damage the plants and is not desired. The salinity content was also found to be higher in the sample from Kinnaird College which was 8.99 dS/m compared to the standard of 4.0 dS/m. The compost sample of Lahore Compost Private Limited was found to be the best among the tested samples
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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 | 0.001 |
| 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 it