A Comparative Analysis of Ontario Cider Producers Information Sources and Production Practices
Bibliographic record
Abstract
During the spring of 2000, a telephone survey was conducted with 15 Ontario apple cider producers to gain a better understanding of current production practices and cider information sources. The survey covered sales figures and location of sales, orchard management, facilities, fruit processing and storage, preservation methods, cider testing, additional safety measures and information on outbreaks and regulations. Small, seasonal operations in Ontario produce approximately 20,000 litres of cider per year and some use improper processing procedures. These include using unwashed apples, not using sanitizers, and not properly labeling or using expiration dates or lot numbers. Most did not pasteurize or have additional safety measures. Larger cider producers run year-long with some producing over 500,000 litres of cider. Most sell to large retail stores and have implemented extra safety measures such as HACCP plans, cider testing and pasteurization. Government information is received on an irregular basis by all producers and motivation to ensure a safe, high-quality apple cider is influenced by financial stability along with consumer and market demand, rather than government enforcement.
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".