Tracking U.S. Grain, Oilseed and Related Product Exports in Mexico (Summary)
Bibliographic record
Abstract
Texas A&M AgriLife Research and Texas A&M Transportation Institute scientists found that rail continues to be the most important mode of transport for U.S. grains, oilseeds, and products entering Mexico, followed by seaports and trucks. Nearly all Mexican land ports of entry are connected with a U.S. railroad, except for Nuevo Progreso, which does not have rail access (Fig. 1). Increased rail efficiency caused by larger trains and gauge uniformity facilitates North America Railroads (Canada, United States, and Mexico) integration. Once inside Mexico, the majority of the U.S. exports were shipped by rail within Mexico to their final destination (Fig. 2). Two major Mexican rail companies: Ferromex/Ferrosur and Kansas City Southern de Mexico handled U.S. grains, oilseeds, and related products inside Mexico. Jalisco is the largest single destination for rail shipments, followed by Queretaro, and the Estado de Mexico. The largest rail origin-destination pairs, with at least a million metric tons, include Nuevo Laredo-Queretaro, Piedras Negras-Jalisco, Veracruz-Puebla, Nuevo Laredo-Nuevo Leon, Nuevo Laredo-Estado de Mexico, and Ciudad Juárez-Jalisco.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".