Hydrogeological data evaluation and solid waste management at Al-Akeeder landfill site, Jordan: assessing pollution risks.
Bibliographic record
Abstract
Large quantities of waste from urban, municipal and industrial sectors are generated worldwide and disposed of in landfill sites. This can cause significant problems for groundwater as contamination can occur by infiltration recharge. The present research addresses a concern through an assessment of the pollution risk to the aquifer system at the Al-Akeeder site based on hydrogeological data and solid waste management. We consider the following risk elements: potential rates of waste input, leachate collection system, type of refuse, physical state of the refuse and its water content, monitoring system, disposal criteria and final cover. The environmental compatibility is estimated by applying an integrated method based on the depth of the water, which is the depth of the piezometric level relative to the ground surface (the SINTACS ratings relative to this parameter decreases with increasing depth), the effective infiltration, the unsaturated zone attenuation capacity, the soil attenuation capacity, the hydrological characteristics of the aquifer system, the hydraulic conductivity range of the aquifer, and the hydrologic role of the topographic surface average slope. The intrinsic vulnerability of the aquifer system within and around the study area is at a medium level. We suggest remediation measures to overcome the risks in the study area.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".