Bibliographic record
Abstract
D uring the last Canadian war, Genpral Putnam, the famous partisan soldier, made the first descent upon Goat Island. A wager had been, laid, that no man in the army would dare to cross the Rapids from the American side; and with the personal daring for which he was remarkable, above all the men of that trying period, he undertook the feat. Selecting the four stoutest and most resolute men in his corps, he embarked in a batteau just above the island, and with a rope attached to the ring-bolt, which was held by as many muscular fellows on the shore, he succeeded by desperate rowing in reaching his mark. He most easily towed back, and the feat has since been rendered unnecessary by the construction of the bridge from which the accompanying view is taken. Many years since, a Tonemanta chief, after a violent quarrel with his squaw, lay down to sleep in his canoe. The little bark was moored just out of the tide of Niagara river, at the inlet to the creek which takes its name from his tribe, and the half-drunken chief, with his bottle of rum in his bosom, was soon fast asleep among the sedges. The enraged squaw, finding, after several attempts, that she could not get possession of the bottle without waking him, unmoored the canoe, and swimming out of the creek, pushed it before her into the swift tide of the river.
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 | 0.014 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.047 | 0.007 |
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".