Canada's Maritime Provinces : Nova Scotia, New Brunswick, Prince Edward Island, and Newfoundland.
Bibliographic record
Abstract
THERE'S gripping Romance in the Sea!In the far-off days of the galley and the caravel, and later the square rigger and clipper ship, it spelled adventurethe thrill of dis- covery, the challengeof the unknown.On its heaving bosom rode ships laden with silks and spices, gold and gems.Its un- charted ways were the haunts of roving buccaneers and the danger of pirate ships was added to the terror of its varying moods.The square riggers are gone, or almost so, but the romance lives on, and here in splendor, ribbon-like farms, extending from the railway line to its shores testifying to the historic background of the early seigneurs and settlers, give place to gorgeous woodland scenery as the base of the Gaspe Peninsula is passed, through the scenic gem of the Matapedia Valley and then along the North Shore of the Province of New Brunswick, the largest of the three Maritime Provinces.Montreal is particularly happy as a gateway in that so many Canadian National services converge there. The International Limited and Maple Leaf fromChicago and intermediate points, Detroit, Buffalo, Toronto-are popular alike with the tourist visitor and the business traveller.From Washington, Baltimore, Philadelphia and New York, the "Washingtonian" -from Boston, the "Ambassador" and "New Englander" afford convenient connections at Montreal R.M.S. "Lady Drake" in Halifax Harbour.
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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 | 0.002 | 0.007 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.071 | 0.008 |
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".