Bibliographic record
Abstract
Kelowna, a beautiful sight facing the Okanagan Lake is, since 1981, a Sister City of Kasugai City of Central Japan. Kelowna, a multi-cultural City which refused to send to the Camps the citizens of the Japanese Community during WWII. We wish to learn and benefit from Kelowna's multi-culturalism. Its multi-cultural wineries include French, German and Italian wines. The First Nation also owns its-own winery. The Japanese Community in Kelowna do not produce wine and specializes in fruits farming. Among its products we wish to introduce to Central Japan the hascap berries, hybridized with Russian berries in Canada, coming originally from the Ainu Indigenous People of Hokkaido who is culturally very close to the Kelowna First Nation. Tasting Kelowna wines and hascap allows us to enjoy the multi-cultural tastes of Kelowna. It meets the SDGs Objectives 16 and 17, by developing an all-inclusive multi-cultural exchange crossing all national and cultural borders. Chubu University is in the process of developing hascap jam of less sugar content. Its nutritious value is high, the preservability is guaranteed, and a few tasting experiments prove that its taste is appreciated by the Central Japan consumers. (Seiko Hanochi)
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.088 | 0.037 |
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".