Bibliographic record
Abstract
[2]<br><br>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<br><br>[in margin: Your old friends and fellow [illegible] [passengers?][illegible] Mrs [Patton?]]<br><br>[in margin: 73]<br><br><br><br> [4]<br><br>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.<br><br>[in margin: your friend Abby [H. Patton?]]<br>
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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 teacher head, 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".