2019 Northeast Maple Business Benchmark
Bibliographic record
Abstract
The 2019 production season rebounded with a 1% increase in US national syrup production from the previous crop in 2018. Bulk market prices continued a slow decline following reports of a strong 2019 crop year and continued strength of the US dollar. General reports from maple producers and sellers indicate an environment of increasing competition for sales. Successive strong crop years bolstering supply, downward price pressure from Canadian import dynamics and more US producers pursuing direct and wholesale market channels reinforced the increased competition. By 2019 the signals to maple owners were clear, business performance in the modern maple era will be impacted by an evolving marketplace. The 2019 Northeast Maple Business Benchmark report documents the seventh year of financial record analysis for a small group of commercial syrup producers. In 2019 the project has shifted to include maple produc-ers in Vermont, Maine, New Hampshire and Massachusetts. An increasing focus is placed on larger scale enterprises in 2019 and participants had to generate at least $100,000 in annual gross sales to be included in the study. This report demonstrates key management and financial metrics including: yield statistics, land use, operating costs, investment requirements, total cost of production, marketing strategy and net income. See additional publications to view the other annual Northeast Maple Benchmark reports.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.056 | 0.034 |
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".