Bibliographic record
Abstract
O n reaching Boston I found that my passage had been taken in the Cunard steamer America , reputed to be the slowest and wettest of the whole line. Some of my kind American friends, anxious to induce me to remain for the winter with them, had exaggerated the dangers and discomforts of a winter-passage; the December storms, the three days spent in crossing the Newfoundland Banks, steaming at half-speed with fog-bells ringing and foghorns blowing, the impossibility of going on deck, and the disagreeableness of being shut up in a close heated saloon. It was with all these slanders against the ship fresh in my recollection that I saw her in dock on the morning of my leaving America, her large, shapeless, wall-sided hull looming darkly through a shower of rain. The friends who had first welcomed me to the States accompanied me to the vessel, rendering my departure from them the more regretful, and scarcely had I taken leave of them when a gun was fired, the lashings were cast off, and our huge wheels began their ceaseless revolutions. It was in some respects a cheerless embarkation. The Indian summer had passed away; the ground was bound by frost; driving showers of sleet were descending; and a cold, howling, wintry wind was sweeping over the waters of Massachusetts Bay. We were considerably retarded between Boston and Halifax by contrary winds.
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 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.324 | 0.181 |
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".