Bibliographic record
Abstract
Organic chemicals are used in all types of industries including but not limited to automotive and engine repair, dry cleaning, asphalt operations, dye manufacturing, agricultural activities, and food processing. The usage of organic chemicals is of increasing concern to regulators because of the contamination of soil and groundwater resulting from the mishandling and disposal of these chemicals. Typically, drums, underground and aboveground tanks are used to store these organic chemicals. The presence of large amount of chemicals, gasoline, and diesel fuel on-site is considering an indicator of the potential for soil and groundwater contamination. Due to leaking UST or surface spills of organic chemicals and its constituent it becomes the common culprits of soil and groundwater contamination. The first step toward implementing a remediation is to provide for [...] a better understanding of the physical properties of the organic chemicals themselves. This study reviews the chemistry of hydrocarbon and the fundamental concepts and principles of geology and hydrogeology, since the media where the contamination is taking place, and followed by a discussion of the fate and transport of contaminants in the subsurface, from an industry and regulatory point of view. The types and design of remediation system was overlooked and applied in a real case of study of Phase I, II and [III] Environmental Site Assessment. This study concluded with an overview of the application of the remote sensing in the environmental industry and its future potential involvement in the environmental site assessments, Phase I through Phase III audits.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".