Methodology to Characterize Agriculture-Related Trucking on Low-Volume Rural Roads to Support Asset Management
Bibliographic record
Abstract
This paper develops a methodology to characterize agriculture-related trucking on low-volume rural roads. The methodology considers truck trips from the field to intermediate storage facilities (field-to-storage) and from these facilities to market (storage-to-market). The methodology, which applies the transportation systems analysis approach, leverages knowledge from local producers through in-person interviews to qualitatively and quantitatively characterize the transportation supply and demand that generate truck flows. Flow characterization in terms of truck volumes and trip-making characteristics supports asset management decisions, such as maintenance timing and upgrade investments, in addition to providing information for forecasting future demand and infrastructure impacts. The development and application of the methodology contributes in three ways. First, it characterizes truck flows from field-to-storage, a segment of the agricultural supply chain seldom considered by previous research. Second, it demonstrates the extent of information concerning road usage and impacts available from producers. Third, results from the application of the methodology to a study region in Manitoba reveal that: (a) smaller truck types are more commonly used for the shorter field-to-storage trips than storage-to-market trips; (b) actual distance traveled exceeds desired distance traveled, owing mainly to infrastructure-related regulatory constraints; and (c) trip length distributions for the storage-to-market segment exhibit a relationship between trip length and type of truck and commodity. The methodology is transferrable across jurisdictions and scalable for different geographic and temporal scopes. The specific results presented in this paper, however, may not be representative of conditions in other regions.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| 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) | 0.005 | 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".