Retrofit of an Existing Container Yard to Accommodate Automated Stacking Cranes—Manzanillo International Terminal, Panama
Bibliographic record
Abstract
Advances in technology and information management systems have led to the evolution of automated stacking cranes (ASCs) for container handing operations. ASCs have become the current trend in new container yard development because automation increases container yard operational efficiency, improves safety, and can increase container stacking density. While ASCs have several operational advantages over traditional container handling equipment, the civil, electrical, and data infrastructures necessary to support this type of equipment are substantially different. Due to the unique infrastructure needs of an ASC operation, implementation of this technology has been primarily confined to greenfield sites or new terminal redevelopment. As the benefits of ASC operation become more recognized, existing owners of traditional container yards will seek to maintain competitive advantage in the container handling market and introduce automation into existing and fully operational container yards. The purpose of this paper is to highlight some of the major challenges and lessons learned in traditional container yard retrofit for automation through an ASC project completed by Manzanillo International Terminal (MIT) in Manzanillo, Panama.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".