Agricultural Refrigerated Truck Quarterly Report, May 2026
Bibliographic record
Abstract
USDA reported fourth-quarter 2023 U.S. truck shipments of fresh produce were 9.06 million tons-3 percent lower than the previous quarter, but 2 percent higher than fourth quarter 2022.Also, in fourth quarter 2023, shipments from Mexico were 2.66 million tons, which was higher than from any other reported origin.Shipments from Mexico accounted for 29 percent of the total reported shipments of fresh fruit and vegetables.Shipments from the next five top regions by volume were as follows: California, 2.19 million tons (24 percent of the total); Pacific Northwest (PNW), 1.87 million tons (21 percent of the total); Canada, 520,000 tons (6 percent of the total); Arizona, 409,000 tons (5 percent of the total); and finally, Florida, 291,000 tons (3 percent of the total).These top five commodities accounted for 40 percent of reported truck movements in fourth quarter 2023: ► Potatoes (13 percent) ► Apples (10 percent) ► Dry Onions (6 percent) ► Oranges (4 percent) ► Tomatoes (4 percent) Truck RatesThe table below provides a snapshot of quarterly truck rates for U.S. produce shipments over four mileage categories-1-500; 501-1,500; 1,501-2,500; and 2,501+ miles.Please note the U.S. average truck rates provided below were calculated using weighted regional rates and volumes. Diesel FuelDuring fourth quarter 2023, the U.S. diesel fuel price averaged $4.24 per gallon-down 1 percent from the previous quarter and down 16 percent from the same quarter in 2022.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.132 | 0.102 |
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".