Bibliographic record
Abstract
“H ave you seen the Falls?”—“No.” “Then you've seen nothing of America.” I might have seen Trenton Falls, Gennessee Falls, the Falls of Montmorenci and Lorette; but I had seen nothing if I had not seen the Falls ( par excellence ) of Niagara. There were divers reasons why my friends in the States were anxious that I should see Niagara. One was, as I was frequently told, that all I had seen, even to the “ Prayer Eyes ” would go for nothing on my return; for in England, America was supposed to be a vast tract of country containing one town—New York; and one astonishing natural phenomenon, called Niagara. “See New York, Quebec, and Niagara,” was the direction I received when I started upon my travels. I never could make out how, but somehow or other, from my earliest infancy, I had been familiar with the name of Niagara, and, from the numerous pictures I had seen of it, I could, I suppose, have sketched a very accurate likeness of the Horse-shoe Fall. Since I landed at Portland, I had continually met with people who went into ecstatic raptures with Niagara; and after passing within sight of its spray, and within hearing of its roar—after seeing it the great centre of attraction to all persons of every class—my desire to see it for myself became absorbing.
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 | 0.003 |
| 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 | 0.007 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.327 | 0.184 |
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".