Bibliographic record
Abstract
report came to be through my own personal interest in the cider business: its history, my fascination with cider’s unique place as an agricultural value-added product, the people who make it, and the possibilities for British Columbia’s cider industry. What started as a research project became a hobby venture among four college friends intent on making great cider. Once the first batch of our cider trials had been fermented, kegged, and tasted, they were unanimously declared to be quite good, prompting further investigation into the potential commercialization of our product. With a tangible cider in the bottle, and a reasonable understanding of what urban cider production could look like gained through visits to likeminded producers already working on a commercial scale in the Greater Seattle and Portland areas, my team has sought to determine how best to replicate the American Pacific Northwest urban cidery model in BC, specifically in the Greater Vancouver Area. Along our cider journey, we have identified a large annual surplus of cull grade apples at BC Tree Fruits ’ cold storage facility in Oliver, BC: varietals of apples that American entrepreneurs have been using with great success to create popular cider brands. BC Tree Fruits has anecdotally indicated that they are also in the process of getting into the business of cider production, reaffirming our thought that ‘now is the time ’ to be making cider in BC. Rather than pursuing an ‘orchard to glass ’ business model, as is generally employed by BC’s current cideries, my team seeks to ‘bring the orchard to the city’, or, more specifically, launch a microbrewery-style cidery in East Vancouver, creating a yet-to-be-realized cider business model in the province.
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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.606 | 0.462 |
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".