USING NAUTICAL CHARTS TO VISUALIZE 19 TH CENTURY CHANNEL CHANGE ON THE ST. MARYS RIVER, SAULT STE. MARIE, CANADA AND U.S.A.
Bibliographic record
Abstract
The St. Marys River is a key international historic and geographic feature that has attracted little academic interest. In the Sault Ste. Marie area nineteenth century river navigators were blocked by high falls, therefore the channel was altered with locks and canals to aid navigation and enhance commerce. Visualizing spatial temporal change by comparing and analyzing historic cartography is difficult, and results in a lack of detailed knowledge regarding any sequence of changes. A fundamental step in analyzing temporal channel change is the creation of a historic GIS database (HGIS). Using ESRI software only, the 1913 International Waterways Commission (Boundary) Map was converted into raster DEM, TIN and 3-D models to create a visual representation of the historical bathymetric point data. To determine which model best interpolates soundings, the models were compared using RMSE. Draping the 1913 base map over the 3-D model creates a physical landscape for the sounding values. Combining other historical maps with the 3-D model enables a comparative visualization of the channel topography, and illustrates the evolution of channel and bed change. The gathering of international data into a temporal HGIS will provide a platform from which other users can store and analyze additional cartographic information. The use of a temporal HGIS to create a chronology of channel change will turn the 19 th century history of the St. Marys River into a new dynamic and visual experience.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".