To Grandpa Stuiv ACKNOWLEDGEMENTS
Bibliographic record
Abstract
Now here is the part of my thesis that I have been thinking about since the first day I arrived at Caltech. Depending on your outlook, this section may be just as important as the rest of the thesis, as it is an excellent measure of my own personal growth in the last 5 years. Sure, the science beckons me to this place, but when the science does not work, it is the relationships I have made with others that convince me to stay. John Bercaw’s shining reputation in the scientific community is every bit earned. He is a mentor’s mentor (literally … the Bercaw mafia is spread far and wide, from Canada to South America, and more than a few alum in Europe), a beacon of scientific integrity, and, without a doubt, one of the most creative and forward-thinking scientists I have ever known. Although I will never reach John’s intellectual heights, he has taught me to think critically about chemistry. Even more importantly, he has taught me to think constructively. All this stems from his commitment to mentorship in and outside of the classroom. Perhaps this is why he allowed an organic chemist who wanted to learn a little bit more about organometallic mechanism into his group…. I cannot express enough
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 | 0.003 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.008 |
| Insufficient payload (model declined to judge) | 0.282 | 0.230 |
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".