Bibliographic record
Abstract
so fast & is so well appreciated & that the good old Kirk is so well cared for. I enclose P.O. for 20 dollars to help the poor, perhaps Mr Buchanan to whom I send my best respects & wishes for his health will help you to distribute it. We are all about as usual. Maggie is still in delicate health but is able to be about her housework. David is prospering in his California home & I hear that the Portage folk are in their ordinary fairly good health. I have been gone nearly 3 months studying the forests in New England & the southern states - Maryland, Delaware, New Jersey, Virginia, North Carolina, South Carolina, Georgia, Alabama, Tennessee, Kentucky, Florida, etc. Was twice in Canada at Montreal & Quebec Went East bt the Great Lakes & came home by Mobile & New Orleans. Saw millions of Gods finest trees in all their glory of Autumn purple & gold. Remember me to Mrs Kelly with whom I sympathize in her illness & present my congratulations to Mr Kelly on his [manager?] & give my love to Susan & Jan Mather & my respect to Mr [Notman?] Let me soon hear from you again, Love to Maggie Ever affectionately Your Cousin John 02514
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.865 | 0.797 |
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".