Bibliographic record
Abstract
Intermodal terminals in U.S. West Coast ports have experienced remarkable growth in container processing, from 14.2 million containers in 2001 to 22.6 million in 2006. This growth, however, has not come without difficulties. Larger container ships, growing container volumes, and the implementation of new technologies adversely affected West Coast intermodal terminals in their ability to process peak-season container volumes efficiently. In addition to these difficulties, there were major labor disruptions in 2002 and again in 2004. The combination of growing container volumes and the possibility of additional labor disruptions focused attention on diverting containers from West Coast container terminals to container terminals in ports in Canada or Mexico. This review examines the impact of growing container ship size on intermodal terminal infrastructure and explains the labor disruptions in 2002 and 2004. Critical terminal infrastructure at U.S., Canadian, and Mexican container terminals is then summarized. Terminal infrastructure on the west coasts of Canada and Mexico is found to be significantly less than U.S. West Coast terminals and to be capable of processing only a small percentage of containers bound for the West Coast in the event of another major disruption.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".