North American Specialty Cut Flower Production and Postharvest Survey
Bibliographic record
Abstract
In the United States and Canada, there has been an increase in the demand for local specialty cut flowers and a corresponding increase in production. To assess the needs of the industry, we electronically surveyed 1098 cut flower producers and handlers in the United States and Canada regarding their current cut flower production and postharvest problems, and customer issues. We received a total of 210 responses, resulting in a 19% response rate. The results showed that the main production problem was insect management; crop timing was the second most important problem and disease management was the third. Crop timing encompasses a range of related issues such as determining the correct harvest stage, harvest windows that are too short, flowering all at once, or lack of control when the crop is ready to harvest. The main postharvest problems were temperature management, hydration, and flower food management. Timing and stem length were the two most mentioned species-specific production issues, with each one listed by 10% or more of the respondents for eight of the total 31 species. Regarding on-farm postharvest handling, hydration and vase life were the two most mentioned issues; they were reported for five and three species, respectively. For postharvest during storage and transport, damage and hydration were the most common issues; these were listed for three species each. The most commonly mentioned customer complaints were vase life and shattering, which were reported for six and two species, respectively. These results will allow researchers and businesses to focus on the major cut flower production and postharvest issues and on crops that are most in need of improvement in North America.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.005 | 0.001 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".