Bibliographic record
Abstract
Leachate data for the Trail Road sanitary landfill were obtained for stages three (3) and four (4) of the landfill, located in the City of Ottawa, for the period of 10 years from 1996 to 2005. Data included several parameters such as pH, BOD, COD, Ca, Fe, Cl, SO₄, some selected heavy metals such as: Cu, Zn, Pb, and other parameters like Toluene and Vinyl Chloride. Analysis was performed to these data using Microsoft Excel analysis tools. Various graphical and statistical techniques such as Correlation, regression, and contaminant specific analysis were used to characterize leachate from the Trail Road Landfill. The data collected were fitted with trend lines to represent temporal variations. Pearson Product Moment correlation analysis as a multivariate statistical method was later used for identifying linear relationships between the quality of leachate with respect to the water infiltration to the waste, calculated from monthly precipitation data. Results from this research yielded noteworthy temporal variations of many parameters in leachate over the study period. Also, the effect of many factors like the net water infiltrating waste from precipitation and methanogenesis of leachate on the behaviour of leachate parameters was noticeable.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".