New Directions for Strawberry Research in the 2020s
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Advances in the areas of strawberry breeding, production, and pest management are the subjects of new research presented every four years at the North American Strawberry Symposium (NASS).The NASS is an international conference hosted by the North American Strawberry Growers Association; the 2023 symposium was held in San Luis Obispo, California, USA.This editorial review is intended to serve as an introduction to the research topics and institutions represented at the 2023 NASS, and the corresponding Special Article Collection published in the International Journal of Fruit Science.The previous three NASS conferences examined extending production seasons, tailoring production systems to growing environments, and promoting soil health.The 2023 NASS explored new developments in genomics-informed breeding, production automation, and alternative pest management among other topics of keen interest for the strawberry industry.There was strong representation from the host state of California, including research teams from the Cal Poly Strawberry Centre, the University of California Davis, and several private agri-tech ventures.As well, presenters from Pakistan, South Africa, Italy, and Australia brought a global perspective to current breeding, production, and pest management challenges.This NASS 2023 Special Article Collection highlights the major themes of pesticide reduction, data science, and climate change, which are key targets for applied strawberry research in the 2020s.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it