A GIS MAPPING TOOL FOR THE PRESENTATION AND ANALYSIS OF COASTAL DATA ALONG THE SHORELINES OF THE NORTH AMERICAN GREAT LAKES
Bibliographic record
Abstract
In 1997 and 1998, the U.S. Army Corps of Engineers initiated an intensive coastal data collection and modeling activity of Great Lakes shorelines, beginning with Lakes Michigan, Erie and Ontario. A wealth of information is being collected including kilometer-by-kilometer data on recession rates, land use, land use trends, shore type (geology), type and quality of shore protection, offshore geology, and bluff characteristics. To manage and analyze this data, a Recession Rate Analysis System (RRA) was developed. The RRA is a flexible and customizable program that integrates a relational database management system with a dedicated GIS package that allows basic mapping and visualization of all query results. Initial versions of the RRA have utilized MS FoxPro as the database tool and QuikMap as the GIS viewing package. Subsequent versions are intended to be developed for all the Great Lakes and will be referenced to U.S. Army Corps MicroStation graphic files and be directly supportable by industry standard GIS viewing software including both ArcView and GeoMedia. We are also examining the possibility of developing the RRA as a GIS web query tool to provide the same functional capabilities as the CD version (or greater) across the Internet to any user, including shoreline property owners.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.075 | 0.017 |
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".