Miss Canada. Is Canada a land of sunshine or snow?.
Bibliographic record
Abstract
Andthe velvet peach in its swaying" nest fills the gardener with delight.She can pluck, if she will, at Yule tide, in the balmy air. the rose, And the people smile when they hear her called ;t Our Lady of the Snows."The wire that brought that message on lightning under the sea Had been too short to bear it to her furthest boundary.Not by a flippant phrasing of catchword verse or prose.Can the truth be told of the vast domain of " Our Lady of the Snows."-Arthur Weir, in ,; Montreal Star."CSanaMan ^rofcucts, ESIDES an immense Export trade in Flour, Cheese, Butter, Eggs, and Canned Fish, all of > >-s-® which are well-known in England, Canada grows a large quantity of Fruit.Canadian Apples are now very much appreciated, and her Peaches, Plums, Pears, and Berries are equally nice.Not only so, but a large trade is now being done in Canned Tomatoes, Peas, French Beans, and other vegetables -indeed, there seems no limit to the possibilities of development in this direction.Canadian Beef, Mutton, Bacon, Hams, and Poultry are so like English, being fed as on our own English farms, that very few know the difference.And why should they care ?Are not our Canadian brothers as British as we are, and their produce equal to our own ]
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.418 | 0.131 |
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".