Maximum affordable quota prices, concepts and scenarios for the Ontario dairy industry
Bibliographic record
Abstract
This thesis provides a conceptual framework for calculating maximum affordable quota prices by specifying and examining quota purchase scenarios in the Ontario dairy industry. This study is motivated by changes in the market for quota and concerns regarding high quota market prices. Maximum affordable quota prices are based on capital budgeting techniques using individual farm characteristics. Maximum affordable quota prices for four farm adjustment scenarios: over-quota, excess capacity, partial farm expansion and full farm purchase, were calculated at various interest rates, payback periods, farm cost structures and quota resale values. Key variables include farm cost structure, returns to operator labour, time period and quota resale value. In addition, income taxes and risk influenced the maximum affordable quota prices. For example, with a 10-year payback, returns to operator labour, positive quota resale value, an excess capacity producer can afford (after tax) up to $16,155-$19,640 per kg for quota, while the partial farm expansion can afford up to $12,718-$16,349 under similar conditions.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".