Bibliographic record
Abstract
T he Arabian , by which I left Toronto, was inferior to any American steamer I had travelled in. It was crowded with both saloon and steerage passengers, bound for Cobourg, Port Hope, and Montreal. It was very bustling and dirty, and the carpet was plentifully sprinkled with tobacco-juice. The captain was very much flustered with his unusually large living cargo, but he was a good-hearted man, and very careful, having, to use his own phrase, “climbed in at the hawse-holes, and worked his way aft, instead of creeping in at the cabin window with his gloves on.” The stewards were dirty, and the stewardess too smart to attend to the comforts of the passengers. As passengers, crates, and boxes poured in at both the fore and aft entrances, I went out on the little slip of deck to look at the prevalent confusion, having previously ascertained that all my effects were secure. The scene was a very amusing one, for, acting out the maxim that “time is money,” comparatively few of the passengers came down to the wharf more than five minutes before the hour of sailing. People, among whom were a number of “unprotected females,” and juveniles who would not move on , were entangled among trucks and carts discharging cargo—hacks, horses, crates, and barrels. These passengers, who would find it difficult to elbow their way unencumbered, find it next to impossible when their hands are burdened with uncut books, baskets of provender, and diminutive carpet-bags.
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 | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.318 | 0.133 |
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".