Bibliographic record
Abstract
There I was, 21 years old, standing at the base of the tower and staring up 33 floors to the top of Shell Centre. Kind of symbolic—starting my career and visualizing the long climb ahead. Just another new graduate from the University of Saskatchewan working in Calgary as a reservoir engineer. I felt daunted by all the very smart and very experienced people around me. And, like all my young and eager friends, I too wanted to load up my resume with as much experience as possible, and as soon as possible. But how to do that? The big projects that we worked on as parts of a multidisciplinary team were exciting and great learning opportunities, but they developed over a long time, and time is something young folks are impatient about. But as part of my normal work routine, I soon was exposed to the amazing treasure trove of SPE publications. I found that if you read a paper carefully, and followed its trail of references, and then followed the references in the references, you started to get a good perspective on the technical breadth and depth of our industry. If you read all the papers on a sub-topic, it would lead you to another related topic, and so on. Even though Shell, for example, had an excellent library of technical materials and case histories, nothing could match the SPE paper archives. So I started to use the SPE library as my own personal "experience accelerator." Getting into that mountain of SPE papers might appear an overwhelming task, if you think about it. The society has a library of almost 50,000 papers, which is why my advice to young engineers is not to think about it. But for argument's sake, let's say that about 20% of the papers pertain to your particular field of interest. At one paper per day, and allowing for weekends and vacations, it would take you only 40 years to get through the papers in your technical area ("Forty years? Are you kidding me?") OK, say that only about 20% of the papers in your technical area are the ones you really need to read, but let's also remember that reading and properly digesting even two or three papers per week is a lot of work. So realistically, it might take you only 15 years of reading ("Fifteen years?" "Yeah, that's right, kid, and unfortunately, another 20,000 papers will get published while you were reading, so don't expect to ever get finished. Ever hear of Sisyphus?").
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 | 0.005 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.015 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.023 | 0.016 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.448 | 0.412 |
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".