Challenges to California???s Ports
Bibliographic record
Abstract
IntermodalOver past years, container ships have increased the fastest in size more than any other type of ship.Most mega-ships are too big to be handled at current terminals in California.This challenges the efficiency and speed of operation when ships come into port.Consequences: Congestion, delays, and high C02 emission levels. Panama CanalCalifornia is used as a land bridge for ships instead of passing through the Panama Canal because the original locks were too small for current ships.In 2017, new massive locks at the Pacific and Atlantic ocean entrances that will allow advanced ships easier passage.Consequences: Alternate route for ships from East Asia to Europe, diversion of cargo away from California, and lower productivity. Regional PortsShips can choose to avoid the cost of doing business in California.The Port of Lazaro Cardenas & The Port of Manzanillo (Mexico), and Port Prince Rupert (Canada) offer alternate choices that are less costly than ports in California.These ports give an advantage to shippers wanting to reach inland markets or the East Coast of the United States through rail network connections.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.099 | 0.010 |
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".