Landfarm Performance under Arid Conditions. 1. Conceptual Framework
Bibliographic record
Abstract
The primary disposal method for oily sludge in the Kingdom of Saudi Arabia, which is a major oil-exporting country in the world, is landfarming. It is an attractive method of oily sludge disposal in hot arid climatic conditions. Although landfarming technology was introduced to Saudi Arabia in 1982, no scientific studies have been conducted within the Kingdom to support this decision. The results presented in this paper are based on a comprehensive field experiment conducted under Saudi Arabian environmental conditions. Details of experimental setup and conceptual framework of degradation process based on field observations are presented in this paper. The paper also addresses kinetics of oily sludge degradation in landfarm cells under natural and enhanced conditions in the presence of water, nutrients, and tilling. The 12-month field study showed that weathering (evaporation) and not biodegradation is the overall dominant degradation mechanism occurring in landfarms in the study area. The results of this study showed that up to 76% of the oil and grease (O&G) in the sludge has been lost from soil as a result of weathering. However, the results of this study also indicated the primary mechanism for the loss of C17 and C18 alkanes as compared to branched alkanes was due to biodegradation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".