Bibliographic record
Abstract
E stes P ark . I must attempt to put down the trifling events of each day just as they occur. The second time that I was left alone Mr. Nugent came in looking very black, and asked me to ride with him to see the beaver dams on the Black Canyon. No more whistling or singing, or talking to his beautiful mare, or sparkling repartee. His mood was as dark as the sky overhead, which was black with an impending snowstorm. He was quite silent, struck his horse often, started off on a furious gallop, and then throwing his mare on her haunches close to me, said, “You're the first man or woman who's treated me like a human being for many a year.” So he said in this dark mood, but Mr. and Mrs. Dewy, who took a very deep interest in his welfare, always treated him as a rational, intelligent gentleman, and in his better moments he spoke of them with the warmest appreciation. “If you want to know,” he continued, “how nearly a man can become a devil, I'll tell you now.” There was no choice, and we rode up the canyon, and I listened to one of the darkest tales of ruin I have ever heard or read. Its early features were very simple. His father was a British officer quartered at Montreal, of a good old Irish family. From his account he was an ungovernable boy, imperfectly educated, and tyrannising over a loving but weak mother.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.592 | 0.352 |
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".