Eastern North Dakota Hop Cultivar Evaluations Under Varied Training Density Management Programs
Bibliographic record
Abstract
Expansion of United States hop production beyond the Pacific Northwest, has prompted the need for hop research with a regional focus. North Dakota State University has responded to this demand within the Red River Valley by conducting hop cultivar performance and agronomic management trials. In addition, interest in low-input hop yards have prompted an evaluation in hop production with non-supplemental water sources. Field experiments were conducted in 2017 and 2018 at the NDSU Horticulture Research Farm near Absaraka, ND, USA, to evaluate the growth and yield characteristics of nine commercial hop cultivars in response to varied training densities. Cultivars were planted in 2016 on a non-irrigated six-meter trellis system with data collection occurring in 2017 and 2018. Bines were trained at two, four and eight bines per crown. Before mechanical harvest, plant biomass, plant height, and harvest bine number were recorded. Postharvest, cone moisture, cone size, and yield were determined. It was determined that cultivars produced significantly higher yield kg·ha -1 when trained with eight bines per crown in 2018. However, ‘Nugget’ and ‘Canadian Red Vine’ yielded the highest in 2017 compared with the other cultivars. Furthermore, ‘Nugget’, ‘Canadian Red Vine’, and ‘Cascade’ yielded the highest in 2018 compared with the other cultivars. Relatively low yields within the study have prompted interest repeating the trial under irrigated 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".