Bibliographic record
Abstract
The objective of this project is to present a literature review of hazardous waste transportation and the impact on the environment by studying Canada's regulations and legislations and examining the potential use of GIS in reducing hazardous waste transportation. [sic] It is hard to find a specific definition for Hazardous Waste since the hazard could be generated form [i.e. from] a wide variety of sources. Therefore, the hazardous wastes defined according to the Transportation Dangerous Goods Act as those wastes that due to their nature and quantity are potentially hazardous to the human health and the environment. Hazardous wastes usually contain explosive, volatile, toxic, radioactive and flammable materials, and that therefore, requires special techniques to handle the hazard to avoid creating environmental pollution or health hazards during packing, transportation, and disposal. [sic] The government of Canada and the environmental experts made tremendous efforts to reduce the potential hazardous resulted from handling, shipping, treatment and disposal for the hazardous waste and find out alternatives to control that hazard and avoid any environmental impact. [sic] This paper also presented and discussed some studies that point out the important role of GIS in minimizing the impact of potential hazard and reducing incidents regarding hazardous waste shipments through determination of the short and safety transportation routes. [sic]
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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.013 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".