Bibliographic record
Abstract
[1] Old Fountain Lake Dec 20th /63 I was really glad to hear from you uncle Dan (as they call you here), but was sorry to hear that you were about to leave school. but again I am glad that you [evince?] [illegible] sufficient to shift for yourself by [melodizing?] the daughters & sons of the queens Canada. Still I almost wish that this might reach you before you could sing a single ral, I can easily afford to send you a few Yankee greenbacks as I have but few expences and can maul cord wood from gnarly trees at the time of a pair of dimes per diem. You say that you would like a chatty visit with me well Dan I would not begrudge a gowpen O'grozzets for a chat with you. I am living with M my brother in Carr - and we have often said "I wonder how Dan is getting along. I hope he has Canada shillings enough to continue at school" but your letter told of a dearth of dimes which as I remembered was the very disease which instead of making me sing pruned every sprig of music "clean off me". I hope however it may prove a bow speculation if not just go right back to school till you see a better chance and let me know when your treasury [illegible]
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 | 0.009 |
| 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 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.866 | 0.810 |
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".