Application of a light detection and ranging digital elevation model for defining and mapping lakeshore riparian areas
Bibliographic record
Abstract
Abstract In this study, a Light Detection and Ranging-based Digital Elevation Model was developed to delineate and assess the extent of lakeshore riparian environments across the Southwest regions of British Columbia, Canada. The analysis revealed substantial variability in riparian zone area and extent across diverse physiographic settings, with elevation emerging as the primary driver of riparian extent. Low-elevation lake basins were found to support substantially larger riparian areas, often with extensive wetlands. In contrast, high-elevation lake basins exhibited more confined riparian zones due to steeper topography. A legislated fixed-width riparian reserve did not capture the spatial extent of most modelled lakeshore riparian environments. The results of this study highlight the significant value of high-resolution terrain mapping and modelling data for defining the true extent of lakeshore riparian environments and inform conservation planning strategies for small lake ecosystems. The inclusion of a transferable framework for estimating riparian extent based on modeled median riparian width, elevation and physiographic criteria distinguishes this study from previous LiDAR‑based riparian mapping studies by offering an approach to estimating the extent of lakeshore riparian environments to land managers without access to LiDAR or other high-resolution spatial mapping techniques.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".