Using Dry Ports to Facilitate International Trade in Iran; A Model of Success Factors for Implementation of Dry Ports
Bibliographic record
Abstract
As coastal production costs in many countries, producers are moving inland to remain competitive with other countries. Also, container transport volumes continue to grow, the sea flow generates almost proportional inland flow; the links with hinterland will become critical factors for the seaports functionality. Development of dry ports is an important part of intermodal transport which play an important role in improving hinterlands. Successful implementation dry port depends on identification and description of required capabilities to develop advanced intermediate terminal, discover existing deficiency in these capabilities and their effects of each other. This article fill the gaps of implementation of dry ports by offering a conceptual model. To do so, this current study is done in a complicated process in five stages of: review of literature, Delphi, Gap analysis, fuzzy Dematel and Structural equation modeling (SEM). 17 indexes of Delphi model were extracted and classified in 8 groups. The identified gap and causal relations enabled presentation of a model which was tested and verified by Partial Least Squares (PLS).
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".