Bibliographic record
Abstract
June 18th — Ax excellent mail steam-packet carried us along the northern coast of Lake Ontario, from Toronto to Kingston, from whence I made a geological excursion to Gannanoqui. From Kingston we then descended the St. Lawrence to Montreal. The scenery of the Thousand Islands and of the rapids of the St. Lawrence owe much of their beauty to the clearness of the waters, which are almost as green, and their foam as white, as at the Falls of Niagara. On approaching Montreal we seemed to be entering a French province. The language and costume of the peasants and of the old beggars, the priests with their breviaries, the large crosses on the public roads, with the symbols of the Crucifixion, the architecture of the houses, with their steep roofs, large casement windows, and, lastly, the great Catholic cathedral rising in state, with its two lofty towers, carried back our thoughts to Normandy and Brittany, where we spent the corresponding season of last year. The French spoken in those provinces of the mother country is often far less correct, and less easy to follow, than that of the Canadians, whose manners are very prepossessing, much softer and more polite than those of their Anglo-Saxon fellow-countrymen, however superior the latter may be in energy and capability of advancement. I was informed by a physician at Montreal that the English language has made great progress there within his recollection; and all agree that it would soon become still more general if the seat of government were transferred to that city,—a measure since realized, but which was then only beginning to be discussed (1842), and was exciting no small effervescence of party feeling.
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 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.354 | 0.162 |
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".