OIL COMPOSITION AND PROPERTY DATABASE FOR OIL SPILL MODELING
Bibliographic record
Abstract
ABSTRACT At the request of the US EPA Oil Program Center, the National Exposure Research Laboratory's Ecosystems Research Division (ERD) in Athens is developing an oil spill model that focuses on fate and transport of oil components under various response scenarios. A database of prototype oils for use in models is necessary. This multiple component composition data, however, is not typically available because of complexity of oil composition and the impossibility of immediate characterization in the event of a spill. Thus the creation of a database containing both physical property and chemical composition data for a number of common oils at various weathering percentages is highly desirable. The data set must be based upon fractionation of the oils into groups of compounds with similar structures and properties and further must reflect the changes to the oil over the course of the spill. Since 1984, the Emergencies Science and Technology Division (ESTD) of Environment Canada (EC) has developed a database on various physical and chemical properties of crude oils and petroleum products. Through many years endeavour, the database now contains information of hundreds of oils from all over the world. In 2002, funded by the US EPA and EC, the ESTD and ERD completed the cooperative project “Development of a Composition Database for Selected Multicomponent Oils,” to characterize ten prototype crude oils and refined petroleum products. The present work, Oil Composition and Property Database for Oil Spill Modeling, is a logical extension of the 2002 project. Nine new crude oils in common use and with potential to be spilled in the US waters were selected for inclusion in the model database. Comprehensive physical property measurement and chemical composition characterization have been performed for these oils at four weathered stages of each oil. This project provides the most complete and comprehensive database for the selected oils to date. The new composition data has been integrated into the existing US EPA and EC oil properties database. The results are made available to the public on the world wide web.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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".