Bibliographic record
Abstract
two long cold years of isolation, a fri[end?] was really near in the flesh & that my ey[es?] would be blessed that very day with lig[ht?] from a familiar face, & I started from the house in pursuit. I soon found Gen' Al[vord?] & one of the guides who informed me that Pr[of'?] Butler & [illegible] P Jones had started for the top mount Broderick. I waylaid him at a [illegible] in the trail where he had to pass, near the Nevada rapids. I asked anxiously for Henry but was told that he was not in [the?] valley. Towards evening he came to light am[id?] the rocks half [wet?] groping his way among broken granite & bushes, sleeves rolled up res[illegible] open-hat dangling behind his back etc, on see[ing?] me approach he sat down to wipe the per[prer?] ation from his brow & neck, & enquired the way down the rapids. I showed him the pa[illegible] which was marked with little piles of rock but he did not recognize me. When I [was?] directly in front of him & asked him if he did not know me. He said he thought not but soon changed his mind. He was weary with his day of hard climbing but was very cheerful nevertheless, & on the way to [the?] hotel gleaned delightful handfuls [for?] me from the Poets, & remarked upon the surpassing glories of Yo Semite I was sorry to learn that he [was?] going to leave the Valley in the morning in scandalous haste urged by that man y[illegible] was who is governed like a machine by [military?] c[illegible]. [When he left
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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 | 0.008 |
| 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 | 0.011 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.884 | 0.844 |
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".