Thirty-six Years of Award-winning Vegetable Publication Excellence in ASHS Journals
Bibliographic record
Abstract
The American Society for Horticultural Science (ASHS) Vegetable Publication Award, established in 1985, recognizes the author(s) of the outstanding paper on vegetable crops each year published in ASHS journals by an ASHS member. The goal is to encourage better quality research and more effective communication through writing and publication. Manuscripts published in any of the three ASHS journals are eligible to receive the award. To date, of the 36 awarded papers, 86.5% of the awarded papers were published in the Journal of the American Society for Horticultural Science and 13.5% in HortScience , and no publications in HortTechnology have received the award. Authors from 25 states have received the Vegetable Publication Award, with Florida having the most recipients (eight), followed by California (four), Wisconsin (four), Michigan (three), and Illinois (three). In addition, the Vegetable Publication Award has been presented to papers with authors from Israel (two), Canada (two), and one each from Belgium, Brazil, China, Italy, Japan, and the Netherlands. There is some association between commodities that were the subject of the awarded papers and the highest value vegetable commodities in the United States. Eight of the awarded papers reported studies on tomato (ranked first for value in the United States), four on lettuce (ranked second), and three each on broccoli, (ranked fifth) and sweet corn (ranked seventh). Most of the awarded papers covered topics related to plant physiology and response to stress (18 papers), followed by breeding and genetic resources (eight papers); nutraceuticals, aroma, and volatiles (five papers); genetics and gene mapping (three papers); postharvest (two papers); and only one winning paper focused on production systems.
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 | 0.029 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.015 | 0.004 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.036 | 0.029 |
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".