Petroleomics: MS Returns to Its Roots.
Bibliographic record
Abstract
AS ince its commercial birth, MS has been intimately tied to the petrochemical industry- petroleum producers sell molecules, and therefore oil's chemical composition determines its economic value.The composition of the oil also determines both its upstream (production) and downstream (processing) behavior.Determining the composition of those species that contain the heteroatoms nitrogen, sulfur, and oxygen is especially important, because these species contribute to solid deposition, flocculation, catalyst deactivation, storage instability, and refinery corrosion problems, and these factors affect the efficiency with which we collectively use our finite world petroleum reserve.The supply of "light" sweet crudes is diminishing, and the world oil market is therefore shifting toward "heavier" crudes rich in heteroatoms.Characterization of these heavier crudes is limited because of their immense complexity.For oil companies, compositional knowledge equals power-the power to develop oil reserves more efficiently, predict production problems, prevent pipe fouling and failures, reduce refining byproducts and waste, make money, and better manage the world's oil reserve.National security issues are also of concern because of the political instability of many oil-rich nations.North and South America are relatively secure regions and have substantial petroleum reserves, albeit of lower quality, namely heavy crude oil or tar sand.For example, the province of Alberta, Canada, rests on a substantial petroleum reserve.Alberta tar sands alone contain an estimated reserve of ~2 trillion barrels or ~175 billion barrels of recoverable crude oil, compared with Saudi Arabia's ~260 billion barrels of crude oil.The North and South American sources produce heavy, heteroatomic-rich crudes, and they will continue to do so for the immediate future.Compositional knowledge of these crudes could improve production and processing and subsequently reduce our dependence on Middle Eastern oil.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.177 | 0.169 |
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".