PROPERTIES OF CRUDE OIL AND OIL PRODUCTS (NOT JUST ANOTHER PRETTY DATABASE)
Bibliographic record
Abstract
ABSTRACT When an oil spill occurs, there is an immediate need on the part of spill responders to know the properties of the spilled oil, as these will affect the behavior, fate, and effects of the oil, which will in turn affect the choice of countermeasures. However, it is often difficult or impossible to obtain a sample of the spilled oil, let alone the specialized analysis required to determine its properties, in a manner timely enough to suit the circumstances of an oil spill. Under the scrutiny of the media and the public, answers regarding the identity and predicted behavior of the spilled oil will be expected immediately, if not sooner. In preparation for such emergencies, the Emergencies Science Division (ESD) of Environment Canada has been collecting properties data for crude oils and oil products since 1984. Basic physical properties—density, viscosity, pour point, etc.—and environmentally relevant characteristics—evaporation rates, emulsion formation, chemical dispersibility—are measured. Properties related to health and safety—flash point, volatile organic compounds, sulfur—also are determined. In fact, nearly 20 different types of measurements are made for both fresh and weathered crude oils and oil products. To date data has been collected for more than 400 oils. For ease of access, this information is stored in an electronic database. The database in turn is accessible via the World Wide Web, and is also periodically printed in an easy-to-read catalogue format. The wide variety of data collected in the database also makes it possible to examine both simple and complex relationships that may exist between oil properties and spill behavior. This presentation will review the full scope of information determined and collected by ESD. Using tables and graphs, examples will be presented of the many ways in which this information can be viewed and used by both laymen and experts in the field of oil spills.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.009 | 0.015 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.024 | 0.023 |
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".