Blueberry Producers’ Attitudes toward Harvest Mechanization for Fresh Market
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
The availability and cost of agricultural labor is constraining the specialty crop industry throughout the United States. Most soft fruits destined for the fresh market are fragile and are usually hand harvested to maintain optimal quality and postharvest longevity. However, because of labor shortages, machine harvest options are being explored out of necessity. A survey on machine harvest of blueberries ( Vaccinium sp.) for fresh market was conducted in 2015 and 2016 in seven U.S. states and one Canadian province. Survey respondents totaled 223 blueberry producers of various production sizes and scope. A majority (61%) indicated that their berries were destined for fresh markets with 33% machine harvested for this purpose. Eighty percent said that they thought fruit quality was the limiting factor for machine-harvested blueberries destined for fresh markets. Many producers had used mechanized harvesters, but their experience varied greatly. Just less than half (47%) used mechanical harvesters for fewer than 5 years. Most respondents indicated that labor was a primary concern, as well as competing markets and weather. New technologies that reduce harvesting constraints, such as improvements to harvest machinery and packing lines, were of interest to most respondents. Forty-five percent stated they would be interested in using a modified harvest-aid platform with handheld shaking devices if it is viable (i.e., fruit quality and picking efficiency is maintained and the practice is cost effective). Overall, the survey showed that blueberry producers have great concerns with labor costs and availability and are open to exploring mechanization as a way to mitigate the need for hand-harvest labor.
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 | 0.000 | 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 | 0.000 | 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) | 0.001 | 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 it