Development of a Composition Database for Selected Multicomponent Oils
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
ABSTRACT Multicomponent composition and corresponding physical properties data of crude oils and petroleum products are needed as input to environmental fate simulations. Complete sets of such data, however, are not available in the literature due to the complexity and expense of making the measurements. Environment Canada has previously developed a database of various physical and chemical properties of crude oils and petroleum products. In this cooperative project, ten “typical” crude oils and refined products in common use or transport were identified for subsequent characterization. Measured oil physical properties include API gravity, density, sulphur content, water content, flash point, pour point, viscosity, surface and interfacial tension, adhesion, the equation for predicting evaporation, emulsion formation, and simulated boiling point distribution. The chemical composition of the oils are quantified for hydrocarbon groups, volatile organic compounds, n-alkane distribution, distribution of alkylated polyaromatic hydrocarbon (PAH) homologues and other EPA priority PAHs, and biomarker concentrations. This project will provide the most complete and comprehensive database for the selected oils to date. The new composition data will be integrated into the existing Environment Canada oil properties database. The results will be made available to the public both on the world wide web and as a database on disc.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it