Applying The All-hazard Approach To Managing Sour Gas Emergencies In Alberta And British Columbia
Bibliographic record
Abstract
The concerns about sour natural gas exploration and production include significant environmental and health effects associated with the hydrogen sulphide (H2S) contained in the gas. Methane has a Global Warming Potential (GWP) that is 25 times that of carbon dioxide (CO2) (IPCC, 2007). A person who is exposed to as little as 100 ppm (0.01%) of H2S can experience respiratory irritation, sore throat, and wheezing whereas exposure to concentrations of 1000 ppm (0.1%) of H2S can result in death (Alberta Health Services, 2007). This prompts the question; are the emergency management regulations in Alberta and British Columbia adequate to mitigate the impacts to the environment and human health? A comparative methodology was used to compare the emergency management regulations in Alberta and British Columbia against a known model for managing emergencies, known as the Four Pillars of Emergency Management. This research project suggests that British Columbia has adopted superior emergency management regulations that satisfied the outlined criteria 94% of the time whereas Alberta’s regulations satisfy the criteria only 69% of the time.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.000 |
| Open science | 0.002 | 0.002 |
| 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".