Little Cherry--- DRAFT 3 1/22/07 ...1 BIG LITTLE CHERRY by
Bibliographic record
Abstract
Then, in your opinion, an orchard is not exactly a Garden of Eden? Not in England at any rate. Is it so anywhere — in any part of the world? Yes: in Canada. At least, so I am told. I mean in British Columbia. (Bealby viii) At the turn-of-the-century fruit ranching in British Columbia was considered an ideal colonial alternative for many disenchanted Englishmen who sought independence as well as prosperity. Advertisements extolling the virtues of this gentleman’s occupation abounded in the contemporary literature; and, consequently, many came west to seek their fortune. Often these pioneers purchased land from unscrupulous land brokers who extolled the virtues of fruit ranching in both the Okanagan and the Kootenay. In 1906 Earl Grey, Governor-General of Canada, purchased 54 acres of fruit land on the east side of Kootenay Lake after a personal inspection of the region. This convinced many that, indeed, there was a great future in Kootenay fruit growing. Testimonials such as one from James Johnstone, a pioneer Nelson fruit-grower, also promoted the Kootenay region as a possible Garden of Eden: “I consider the conditions here (Kootenay Lake District) the most perfect for fruit-culture....
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.638 | 0.447 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".