Bibliographic record
Abstract
Academia.edu@TWA Nansen G. Saleri reflects on his career in the oil and gas industry. I entered the world of energy in 1974—many moons ago by any account—with Standard Oil of California (today's Chevron). All my degrees were in chemical engineering, so it made sense to be employed by the refining end of the business. Chevron offered me a choice of two research positions: one in their downstream research facility near San Francisco and the other with the upstream counterpart in southern California. The latter became my "accidental" selection per my wife Marina's fear of San Francisco earthquakes. That choice captures my life. Some of the best things in our lives happen due to luck or accidents. I have no way of knowing where I would be today if my wife's field of knowledge about California's tectonics pushed my decision (and hers) in the opposite direction. What I do know, however, is that I have had a terrific journey—still in full swing—since reporting to work at Chevron Oil Field Research Company on Monday, October 7, 1974. Looking Back My career has spanned three distinct experiences in the oil industry— international oil company (IOC), national oil company (NOC), and now entrepreneurship. In September 2007, I launched the third and present phase of my career as a founder of Quantum Reservoir Impact—a niche-technology, venture-capital startup. My career can be viewed as a grand mosaic with three complementary segments, each with its unique color and accent.
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 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".