Bibliographic record
Abstract
[2] journey very much. I visited Buffalo, friends up in Canada then back to Philadelphia, Atlantic City, and last but not least New York City where I was happy indeed for it is like home yet altho' so many old friends are gone, as are the dear huband and son with whom I used to enjoy so much, I had my dear daughter in law with me. and met so many who were connected with my former life in New York and there is so much of interest in that metropolis as [3] I really found it I dreaded leaving it all and coming way across to my lonely home here. But Nature in California is kinder to one as age comes on. and the blood chills, there are many attractions in this land of so much sunshine, and there are kind hearts every where. When one has lived to see their loved ones pass over into the "better land", the only thing left to comfort is the pleasure to be found in books and the joys wh. are common to every one
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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 | 0.004 |
| 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 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.793 | 0.675 |
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".