Strategic Analysis of the Automation of Container Port Terminals through BOT (Business Observation Tool)
Bibliographic record
Abstract
The port system is immersed in a process of digital transformation towards the concept of Ports 4.0, under the new regulatory and connectivity requirements that are expected of them. As a result of the changes that the industrial revolution 4.0 is imposing, based on new information technologies and the change of energy model, the electrification of modes of transport from alternative energies and the total digitalization of the processes is occurring. This conversion to digital, intelligent, and green ports requires the implementation of the new technologies offered by the market. The inclusion of these enabling tools has allowed the development of automated terminals under a functional approach. This article aims to offer the responsible entities a new methodology (BOT) that allows them to successfully undertake the automation of terminals, taking into account the reality of the conditions of the environment in which they are developed. By quantifying the factors that facilitate or impede implementation, it will be possible to determine the strategy to be followed and the necessary measures to be adopted in the project; constituting, therefore, a novel management and planning tool.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 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".