Bibliographic record
Abstract
This TBD column finds me in a rather different place than I was just a couple of years ago. At that time, I made an agreement with one of my senior engineers that I would keep my safety consulting business going until he reached his retirement age goal and I reached my 65th birthday. At that point, my plans were to “retire” in some way or another. Not completely retire, but reduce my staff and begin working part time instead of full time — and choose more interesting projects. In preparation for this event, my wife and I made a few changes to our living arrangement. This mainly involved paying off the remainder of the mortgage on our house and installing a 7 kW solar array. Those investments resulted in our having almost no mortgage and close to zero energy costs. So far this year, our total electric bill is about $20 after 10 months — including our air conditioning, swimming pool and hot tub electricity use. Now, we can comfortably live on Social Security. We also managed to put aside a retirement nest egg that allows us some flexibility to do things besides just existing on Social Security. I no longer have to work for a living; I now only work for fun.
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.812 | 0.662 |
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".