Disruptions in International Trade: A Perspective on Ports of Entry and Supply Chain Resilience
Bibliographic record
Abstract
United States (U.S.) ports of entry (POEs) and supply chains (SCs) have a prominent trade relationship with a growing desire to improve their operational capabilities. Though trade deals like the U.S.-Mexico-Canada (USMCA) agreement have facilitated trade between these countries, U.S. Customs and Border Protection (CBP) at POEs have also increased security inspections, following the September 11th incident, which have impacted international and global SCs. More recently, the COVID-19 pandemic has caused labor shortages at both sea and land POEs, increasing vessel and commercial vehicle congestion. These POE disruptions have also propagated into the third-party logistics (3PL) of SC networks, which has increased transportation costs. In this thesis, we explored operational improvement strategies from the perspectives of the public sector (i.e., U.S.-Mexico POEs) and the private sector (i.e., 3PL SC networks). The goal of this study was to understand the relationship between transportation disruptions and international trade.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".