Bibliographic record
Abstract
S ome apology is due to the reader, whose attention has been thus long withdrawn from other and more important matters, to follow the adventures of an humble individual like myself. The fault, however, of which I have been guilty may be at once repaired, when I inform him that on our arrival at Bermuda we found Sir Alexander Cochrane, in the Tonnant, of eighty guns, waiting to receive us, and to take the command of the whole fleet. The secret of our destination likewise, which up to that moment had been kept, transpired almost as soon as we cast anchor off the island; and it was publicly rumoured that our next point of debarkation would be somewhere on the shores of the Bay of Chesapeake. Nor are these the only interesting public occurrences of which no notice has as yet been taken. On the 4th of June our little army was reinforced by the arrival of the 21st Fusiliers, a fine battalion, mustering nine hundred bayonets, under the command of Colonel Patterson. On the evening of the 29th a squadron of four frigates and several transports appeared in the offing, which by mid-day on the day following were all at anchor in the roads. They proved to be from the Mediterranean, having the 21st, 29th, and 62nd Regiments on board, of which the two latter were proceeding to join Sir George Prevost's army in Canada, whilst the former attached itself to that under the command of General Ross.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.602 | 0.492 |
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".