Bibliographic record
Abstract
June 14. 1842.— from Queenstown we embarked in a fine steamer for Toronto, and had scarcely left the mouth of the river, and entered Lake Ontario, when we were surprised at seeing Toronto in the horizon, and the low wooded plain on which the town is built. By the effect of refraction, or “ mirage,” so common on this lake, the houses and trees were drawn up and lengthened vertically, so that I should have guessed them to be from 200 to 400 feet high, while the gently rising ground behind the town had the appearance of distant mountains. In the ordinary state of the atmosphere none of this land, much less the city, would be visible at this distance, even in the clearest weather. Toronto contains already a population of 18,000 souls. The plain on which it stands has a gentle, and to the eye imperceptible, slope upwards from the lake, and is still covered, for the most part, with a dense forest, which is beginning to give way before the axe of the new settler. I found Mr. Roy, the civil engineer, expecting me, and started with him the morning after my arrival to examine those ridges of sand and gravel, and those successive terraces, at various heights above the level of Lake Ontario, of which he had given an account in 1837 to the Geological Society of London.
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 | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.446 | 0.224 |
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".