Bibliographic record
Abstract
W e arrived at Port Talbot, Canada West, a day or two ago from Niagara, where we stayed a fortnight. This is a delightful place. We went back to Buffalo, then crossed a part of Lake Erie (we were a day and a night on board the steamer “London”), and, landing at Port Stanley, we had some refreshments at the little hotel there, where we were well taken care of by the particularly attentive and obliging proprietors; and then we came on in a hired carriage through beautiful woods to this beautiful spot. The road, however, was not equally beautiful, and we broke down, which, apparently not un foreseen accident, our driver took very unconcernedly and philosophically, and immediately set about repairing the damage. A carriage breaking down is of little moment indeed in the woods of Canada, where they are usually of a tough and rough kind, and where the charioteer (who, I believe, is generally expected to be somewhat experienced in this way) speedily rectifies the injury by cutting down the first likely tree by the road side, and adapting it to his purpose by some “rough and ready” kind of craft. In this instance I had little doubt but that our damaged vehicle would come out of the hands of our Jehu nearly, if not quite, as good as new; for its “build” was such, that he might very probably have been himself the coach-maker originally, as well as coach-driver and coach-breaker.
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 | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.178 | 0.045 |
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".