Word Count: 5690 +23 (tables and figures)
Bibliographic record
Abstract
Substantial pressures exist to develop new and expand existing container ports. Estimating the potential demand for a new or expanded port is critical for assessing its potential financial and economic success but is extremely difficult due to the many issues which must be considered. Among these issues are multimodal accesses to markets and competition from other ports. Yet, these issues are very difficult to assess without an appropriate model. Developing models for demand analyses of port and related multimodal facilities is an important, long-run task for the profession, but raises many challenges. This paper attempts to contribute to the literature on port demand by applying a newly developed container port and related multimodal computer simulation model. Using readily available and preliminary data for a hypothetical new port at Quonset Point, RI, we apply the model to estimate demand for several cases: (1) with and without multimodal rail access, (2) with and without recognition of competition faced from other ports. Estimated demand for the hypothetical port is small with no Class I rail access, but adding Class I rail access vastly expands the potential market service area and demand for the ports services. On the other hand, including the ports of Halifax and Montreal in the model substantially reduces estimated potential demand at the new port. Hence, port planners who ignore competition from substitute ports will be working
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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.942 | 0.906 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".