Sport and pleasure in the valley of the Ottawa.
Bibliographic record
Abstract
Valleyof the Ottawa ©ANDOUR is always a charming attribute, and that is why I want to tell you at the very outset that my object in writing this sketch is to endeavour to per- suade my readers to experience for themselves the beauties of the Ottawa Valley.It is no new story that I have to tell, for it is now nearly three centuries ago, in the year 1610, since Champlain first sent his men up the Ottawa to learn PARLIAMENT BUILDINGS, FROM MAJOR HILL PARK.the characteristics of the country and its people.These hardy explorers returned to their great chief with marvellously thrilling tales of the beauty and grandeur of the country through which they had travelled.Their great leader was so fascinated by their recitals, that he made up his mind never to cease his efforts until he had explored the route for himself, hoping, as he did, to find at the end a western or northern sea opening up the route to China, which so many had sought in vain.It was not long before this intrepid explorer, accompanied by a small party, set out to realize his ambitions, and commenced the ascent of the great river.Passing through the beautiful lakes, with their ROCKLIFFE PARK, OTTAWA.
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 | 0.021 | 0.018 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".