The Chesapeake and Ohio (C&O) Canal’s Role in Developing the District of Columbia’s Ports
Bibliographic record
Abstract
While the how and why of the Chesapeake and Ohio (C&O) Canal’s birth are important, and it is with much thanks that students of inland waterways (few are we in number) are able to access this accumulated knowledge, it is somewhat shocking that the major role this canal played in developing the Potomac River valley and, most importantly, the country’s capital region, is overlooked. This article will address the pivotal role the C&O Canal played in forming and developing the two main ports that serviced the greater District of Columbia area (encompassing northern Virginia), and, to a lesser extent, the middle Atlantic region. Bien que le comment et le pourquoi des débuts du canal Chesapeake et Ohio (C&O) soient importants et que les amateurs des voies navigables intérieures (peu nombreux sommes-nous) soient très reconnaissants de pouvoir accéder aux connaissances acquises, il est étonnant que le rôle majeur qu’ait joué ce canal dans le développement de la vallée de la rivière Potomac et, plus important encore, de la région de la capitale nationale des États-Unis ait été laissé de côté. Cet article traite du rôle central du canal C&O dans la formation et le développement des deux principaux ports qui desservaient la grande région du district de Columbia (comprenant le nord de la Virginie) et, dans une moindre mesure, la région de l’Atlantique-Centre.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".