Bibliographic record
Abstract
THE SONS OF DANIEL BOONEBRANCH OFCLUB OF CLUBS11 EAST 24 TH STREETNEW YORK CITYflood, or in times of riot or standing up [illegible] noted bully or rough, when the interests of peaceable [illegible] demands it; and so forth, and so forth. You being a particular out-door here, the President [illegible] self endorsed your name as being the one eminently proper for [illegible]Notch for the society. I have accordingly enclosed some suggestions for the deed necessary to win the John Muir Top Notch. I will consider it a personal favor if you will kindly select the one you would like best to be known by your name. This will please the [illegible] immensely and stimulates them to worthy acts and deeds. If you will give your early attention to this so that [illegible] may [illegible] in a position to send [illegible] list of Top Notches to the different Forts scattered [illegible] the [illegible] States and Canada, you will conser a great faver on me. Any suggestion or amendment you [illegible] offer for your Notch will be glade adopte[illegible] It may interest you to know that the only other Top Notches proposed are the Roosevelt Notch, the Dewey Notch, the General Bell Notch, the Buffalo Bill Notch, and the Mark Twain Notch. Sincerely yours, [illegible][03859]
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.871 | 0.814 |
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".