Estimating Natural Boundary Change on a Large Lake
Bibliographic record
Abstract
The natural boundary is intuitively understood to be the line dividing the bed of a water body from the adjacent land. It is used in many jurisdictions to define the boundary of ownership between a land parcel and a state-owned watercourse. It is typically defined on the ground through observations by a professional surveyor. Predicting changes to the natural boundary has obvious utility (e.g., with climate change); however, its’ observational nature does not lend itself to such predictions. This paper uses a case study of a large lake to explore the relationship between shoreline morphology and hydrology, and the elevation of the natural boundary. Significant correlation was found between the natural boundary elevation and average wave power, but not with variables beach slope, sediment type, or presence of vegetation. Below 0.4 W/m of average wave power, the correlation with natural boundary elevation is very weak, suggesting a lower limit of influence. Two process-based methods for estimation of natural boundary change are proposed, one based on the observed statistical relationship between the average wave power and the natural boundary elevation, and another method based on detailed analytical modelling of bottom stress from breaking waves using the Xbeach software (v1.23). Both methods were used to estimate natural boundary change under a hypothetical change in water level regime due to proposed weir upgrades. While observational data are not available to support evaluation, both approaches predict similar changes to the natural boundary despite their significantly different basis. While the modelling approach requires significant analyst and computational effort, the parametric approach is comparatively efficient, making it practical to apply at high resolution over very large shorelines.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".