Bibliographic record
Abstract
This is nuts."Sitting on my motorcycle just after sunrise on a cool, misty August morning, my travel bag stuffed with extra jeans, socks, T-shirts, tools, and maps and strapped to the bike's sissy bar, we-the bike and me-seemed set to take off on a trip across much of the northwestern United States and southern British Columbia.The turnaround point was to be Victoria, on Vancouver Island, where I was to meet my wife and daughter, my sister-in-law and her family, and various of their cousins, nieces, and nephews.I expected the trip to cover about 3,000 miles.I had been planning the trip for months, but now a voice from somewhere deep inside kept saying, "This is nuts.You are in your mid-fifties; you are going to get yourself killed out there; you have a wife and a teenage daughter; have you thought about them?Really?"As I sat there, Reasonable Jim seemed to be getting the upper hand over Irresponsible Jim, insisting that I roll the bike back into the garage, put the travel bag in the car, and head for the airport.But after a couple of minutes of to-be-or-not-to-be, I set good sense aside, fired up the bike's engine, and rolled away.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.332 | 0.115 |
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".