SPATIAL AND TEMPORAL CONSIDERATION FOR CALCULATING SHORELINE CHANGE RATES IN THE GREAT LAKES BASIN
Bibliographic record
Abstract
Accurate long-term shoreline change rates are required for a wide range of shoreline studies and coastal zone management applications in the Great Lakes Basin. However, the literature on methods, techniques for quantifying source errors, guidelines for data acquisition, and new approaches is focused primarily on the sandy coastlines of the eastern and gulf coasts of the United States. Therefore, a comprehensive shoreline change investigation was completed for Ottawa and Allegan Counties, Michigan to investigate issues specific to the fresh water shorelines of the Great Lakes. A detailed spatial database was developed that included 79 km of continuous top of bank and dune crest lines for five temporal periods. Over 70,000 erosion transects were generated and analyzed with customized ArcGIS tools for the sandy and cohesive shore types found in the two counties. Significant spatial and temporal variability in the transect measurements were observed for both shore types. Based on the results, a series of detailed recommendations are provided for selecting historical sources of positional data, minimizing sampling errors by selecting an appropriate transect spacing, considering lake level impacts, and the influence of the bluff failure cycle on recession rates.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".