ASSESSMENT REPORT ON ETHYLENE FOR DEVELOPING AMBIENT AIR QUALITY OBJECTIVES ASSESSMENT REPORT ON ETHYLENE FOR DEVELOPING AMBIENT AIR QUALITY OBJECTIVES
Bibliographic record
Abstract
Ethylene is a naturally occurring compound in ambient air. It is produced at low levels by soil microorganisms, algae, lichens and plants. Other natural sources of ethylene are volcanic activity and combustion in forest and grass fires. In Alberta, the concentration in ambient air resulting from these natural sources is typically low, approximating 12 µg m-3 (equal to 10 parts per billion). Anthropogenic sources of ethylene such as combustion of fossil fuels, and processing of natural gas in petrochemical facilities (e.g. production of plastics) result in emissions that may substantially increase ambient air ethylene concentrations. Alberta industrial facilities reported ethylene releases of 1,238 tonnes in 1999. Monitoring of ambient ethylene around petrochemical facilities is a method that gives an indication of releases of ethylene into the environment. There are many methods of collecting and measuring ambient ethylene. The most important aspect of this process is a good quality assurance and quality control program. The samples must be collected using methods that prevent contamination or loss then must be analysed using methods that will ensure consistency of results. The Laboratory Data Quality Assurance Policy
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 | 0.013 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.008 |
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".