Bibliographic record
Abstract
How did you first land a job in geophysics? Through horses. I'm old enough to have ridden a horse to a country school in the wintertime in Canada because we didn't have snowplows, so we had these workhorses that we rode when the snow was too deep to walk. After I got out of the University of Alberta in 1964, I ended up with some better riding horses. A friend and I were living in a ranch west of Calgary boarding horses as a part-time occupation. One night there was a knock at the door and this fellow we had never seen before asked us, “Do you want to rent some land from us around here, fence it off and you can use it to run your horses?” We said, sure, we'd do that. He looked at my iron ring on my little finger and recognized that I was an engineer; and he had one on too. “What kind?” he asked. “Electrical,” I said. “Oh! We've got this seismic business … this electronics lab … maybe you should come down and talk to me.” I was working for a building contractor at the time, which I didn't find very exciting, so I did. My first assignment was six weeks in the bush in northern Alberta, planting geophones by day and fixing electronics at night. After that I worked in their electronics lab. And that's how I started; it was kind of fluky.
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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.050 | 0.011 |
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".