Grain Transportation Report, November 7, 2024
Bibliographic record
Abstract
UP To Open New Intermodal Terminal in Kansas City. Union Pacific Railroad (UP)is building a new intermodal terminal west of downtown Kansas City, MO.The terminal is expected to open next year.According to UP, the new terminal will "serve both domestic and international containerized shipments of grains, consumer goods, refrigerated products, and pet foods."Via UP's 32,000-mile network, the terminal will be able to reach Mexico, Canada, and overseas markets.According to the Surface Transportation Board's public-use carload waybill sample (available on AgTransport), U.S. railroads originated 4.7 million tons of containerized grain in 2022.In the Kansas City region, U.S. railroads originated 300,000 tons of containerized grain-nearly all of which was destined to Los Angeles, CA, for export overseas. Over $54 Million Awarded to South Dakota Rail Improvement Projects.The U.S. Department of Transportation's Federal Railroad Administration (FRA) recently announced more than $108 million in funding to nine rail improvement projects as part of the Special Transportation Circumstances Grant Program.Six projects (totaling 54.1 million) were awarded to regional (i.e., Class II) and short line (i.e., Class III) railroads in South Dakota-a State that relies strongly on both types of rail transport.According to the Association of American Railroads, South Dakota's regional and short line railroads operate on 1,171 miles of track-54 percent of the State's total freight rail network.Short line and regional railroads provide rail access for rural grain producers and reduce overall reliance on trucks, resulting in lower emissions and less road congestion and maintenance.However, government funding is often needed to adequately maintain short line tracks. FHWA Funds EmergencyRoad and Bridge Repair in the Carolinas and Tennessee.The Department of Transportation's (DOT) Federal Highway Administration recently released emergency funding to repair roads and bridges damaged by Hurricane Helene in North Carolina, Tennessee, and South Carolina: $100 million for North Carolina, $32 million for Tennessee, and $2 million for South Carolina.According to data from DOT's Freight Analysis Framework, more than 17 million tons of grain and animals moved by truck in these three States in 2017.These emergency relief funds, provided through the "quick release" process, are an initial installment of funds toward restoring highways and bridges from damage.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.092 | 0.069 |
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".