Bibliographic record
Abstract
W e arrived at Niagara to-day from Buffalo, and put up at the Clifton House. It will not be expected that I should tell what my first feelings and impressions were on beholding this thrice-glorious cataract, for I hardly am, in the least, conscious of what they were myself. I only know this; it scarcely seemed to me at all like what any painting or any description had represented it to be, except only in the shape of the great Canadian Fall. When the train we were in stopped, the roar of the cataract burst on our ears most majestically. It was a moment of intense excitement, and on we hastened, and stood very shortly within a few feet of the verge of the American Fall, and looking on to the magnificent Horseshoe. There we were in the audience-chamber of the great Water King. If one saw the sun for the first time, could one describe it? Do not expect me yet to say anything of Niagara; at least anything to the purpose. The garrulous mood will very likely come on me presently; when, perhaps, I shall quite tire the reader with my rhapsodies, so that he may have cause to wish all my powers of expression were still frozen up by awe and admiration, like the notes in the horn, as related of Baron Munchausen. What a wonderful thing can water become! One feels, on looking at Niagara, as if one had never seen that element before.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.346 | 0.150 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".