Bibliographic record
Abstract
Groundwater contamination is considered to be one of the most significant ecological and health problems. The contaminant released from the source will move down-gradient of the groundwater flow and greatly affects the general environmental features of large regions and drinking water supply conditions in many countries (U.S. Environmental Protection Agency 1987). \n \nThis project will adopt a case study in Western Canada to review a groundwater contaminant transport modeling and establish a human health risk assessment. To modeling groundwater contaminant transport under uncertainty, the three-dimensional analytical model proposed by Huyakorn et al. (1987) and Monte Carlo simulation will be adopted and applied to a case study. For more efficient and accurate analyzing, computer software called MATLAB will be employed to be the platform of simulation. \n \nFurthermore, a human health risk assessment which include hazard identification, exposure assessment, toxicity assessment and risk characterization will be conducted in this report to predicting human ingestion exposure and its corresponding risk to get a cancer. In the assessment, a exposure calculation model and a cancer risk calculation model came up from USEPA will be adopted for calculation. As it requires parameters of different age intervals, the likelihood calculated is very high and it can be used to determine whether actions should be taken to clean up the groundwater contamination.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".