Comparison of Physio-Chemical Characteristics of Different Compost Samples
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
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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".