Bibliographic record
Abstract
A t half-past two in the morning I wended my way in solitude through the deserted streets of Charlottetown in the direction of the wharf where lay the Shediac steamer. The night was dark, and the walk by no means pleasant, but I am in the habit of taking things as I find them, and making the best of my lot; so I did not repine. When I stepped on board the vessel, I found her, to use strong language, terribly crowded—there being within her upwards of four hundred and fifty passengers. These crowded the decks like flies, so that there was no sitting, and barely standing room ; and when I descended, with considerable difficulty, into the cabin, I beheld an accumulation of legs and arms such as I had never done before in all my travels. The packing was closer than that adopted onboard an African slaver, and the ventilation less perfect. However, I had prepared myself to sail in this steamer, so I endured the “ roughing, ” and stood and sat in a narrow compass, and breathing an unwholesome atmosphere, until the wharf at Shediac was reached, at eleven o'clock. At three in the afternoon I left in the same steamer for Gaspe, the easternmost point of Canada, in order to meet the royal squadron again. She had about one hundred and twenty passengers on board, and sailed in a roundabout route, calling at various places en route .
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 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.372 | 0.217 |
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".