Economic and Management Tool for Assessing Wild Blueberry Production Costs and Financial Feasibility
Bibliographic record
Abstract
Abstract. The wild blueberry industry is facing record low berry prices that has resulted in major concerns for growers, especially in Atlantic Canada and the United States. Farm input and other costs to produce wild blueberries continue to increase, while farmers face record low blueberry prices (in 2016 and 2017). The cost-price squeeze has prompted growers to look for innovative methods to remain financially viable and sustainable. To ensure profitable farm operations, farmers should keep detailed production, management, and financial records that can be used to estimate production, harvest, and marketing costs, but such data and records are not typically compiled by wild blueberry farmers. Spreadsheet-based enterprise budgeting tools have been developed for specific crops by provincial and state extension specialists in Canada and the United States. However, currently there is no such decision tool that accounts for the unique two-year production cycle of wild blueberries, which farmers can use to compile and evaluate input use and rates, and assess production costs and farm economic performance. Keywords: Click here to enter keywords and key phrases, separated by commas, with a period at the end
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 0.004 |
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".