Performance of hazelnut cultivars and selections in southern Ontario
Bibliographic record
Abstract
The global demand for hazelnuts is increasing steadily, driven by increasing use by chocolate companies, pharmaceuticals, health products, and others. North America only produces 5% of the world crop, of which 99% is produced in Oregon (OR, USA). Most available cultivars are adapted to areas with mild winters and thus do not perform well in Ontario (ON, Canada). Our objective was to identify genotypes capable of supporting the newly formed hazelnut industry in southern Ontario. In the last several decades, selections have been identified in ON, New York (NY, USA), and Michigan (MI, USA) that may be better adapted in ON than cultivars from Europe and OR. To test our hypothesis that these new selections would outperform cultivars from Europe and OR in southern Ontario, two trials were performed to evaluate yield, nut quality, and winter hardiness. As hypothesized, selections from ON and NY such as ‘Gene’, ‘Alex’, and ‘Slate’ were winter hardy and highest yielding, followed by ‘Butler’ and ‘Gamma’ from OR; however, these selections did not perform well in other assessed parameters such as nut quality and catkin survivability. Possible explanations for the poor performance of the European and OR cultivars include (i) longer time needed to acclimatize and enter production phase relative to selections from ON, NY, and MI, and (ii) cold susceptibility during the initial establishment phase. The need to allocate resources towards survival probably delays the ability of the European and OR cultivars to enter the production phase. In conclusion, hazelnut cultivars brought from regions with a different climate need to be tested for cold hardiness.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".