(179) An Overview of the Sonoran Vegetable Industry
Bibliographic record
Abstract
Sonora, Mexico, is an outstanding area for growing good quality and high-yield vegetables, fruits, and nuts for year-round exportation. Each year, Sonora produces important, large quantities of fruits and nuts for exportation, including table grape, citrus, pecan, and olive fruit. Also, fresh vegetable production in Sonora is very important. Annually, large volumes of melon, pumpkin, summer squash, chili, husk tomato, tomato, and asparagus are produced for exportation to the United States, Europe, and Japan. Throughout the year, two important growing seasons for vegetable production have been established in Sonora. The most important growing season for vegetable exportation in Sonora is the autumn-winter season, when higher prices are reached for summer vegetables in the U.S. markets. The autumn–winter season begins in August and finishes in December. In Sonora, during the 2002–03 agricultural cycle, 39,666 ha (89,000 acres) of vegetables were established in the field. Many growers in Sonora are investing in imported high technologies for protected cropping from several developed countries, such as the United States, Canada, Israel, and some European countries. Currently in Sonora, high technology is applied by growers for vegetable production, i.e., plastic mulching, low and high tunnels, greenhouses, and shadow frames, which have been frequently used on fresh vegetable commercial production to improve both quality and yield. Because of a large labor force and the attractive income from fresh vegetable exportations to the United States, fresh vegetable production is a very important industry in Sonora. In fact, growing summer vegetables for exportation during the wintertime in Sonora, Mexico, is a good business.
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 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".