Service Time Variability at Blaine, Washington, Border Crossing and Impact on Regional Supply Chains
Bibliographic record
Abstract
Variable service times at vehicle processing facilities (borders, weigh stations, landside marine port gates) cause transportation planning challenges for companies that regularly visit them. Companies must either build more time into their schedules than is necessary, and therefore underutilize their equipment, or risk missing delivery windows or exceeding hours of service regulations, actions that can result in fines, lost business opportunities, or other logistical costs. Border crossing times are examined at Blaine, Washington, between Whatcom County, Washington, and the Lower Mainland of British Columbia, Canada, to assess the variability in crossing times at this border crossing and the impact of this variability on regional supply chains. Variability data collected for bidirectional trade are presented. Directional, daily, hourly, and seasonal variations are examined, and interviews are conducted with regional carriers to better understand the current response to variability, the benefit of a reduction in variability, and how that is related to the goods moved or to other business operating characteristics. This paper describes the level of variability in border crossing times and carriers’ responses to this variability and shows that the primary strategy used, increasing buffer times, reduces carrier productivity. However, this cost is negligible because of the current nature of the industry.
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".