Bibliographic record
Abstract
[2] to look at. Oh yes I forget we have had a great General and expresident and great American traveler for a week and a city full of red, white and blue flags floating from hill top to hill top. We have not known such an oration or so much p[illegible]tion since we sent our best bl[illegible] of the north to west out the accursed slavery Talk of atonement, who ever knew a more fearful atoning for sin than that our nation North and South East and West passed through in the great Rebellion. Mr Patton and I were among the enthusiastic to welcome General Grant. Now we are going in the morning to [star?] command. Stopping at Salt Lake City, Denver – Le[illegible]ville – St Louis & other points on the way to New York. We have read of you in the Bulletin and heard of you through Dr & Mrs Kendall. By the way how you enjoy little hils at the Missionaries. Hope they or the Indians wont kill you – Do write us and do come to New York and be [lionized?] a while [in margin: Your old friends and fellow [illegible] [passengers?][illegible] Mrs [Patton?]] [in margin: 73] [4] to teach other people how to love them. When you come to see us we will sit on a blanket and sing [illegible] Buns songs while you give us the [illegible] [illegible] Scotch [accent?]. I write to say more [illegible] have you to se[illegible] Mr Patton and my love and [illegible] you I bless the day that our eyes saw you and the glories of Alaska. [in margin: your friend Abby [H. Patton?]]
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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 | 0.005 |
| 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 | 0.008 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.851 | 0.775 |
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".