Using the Cartographic Depth-to-Water Index to Locate Small Streams and Associated Wet Areas across Landscapes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
With increasing scarcity of natural resources, there is a need to provide resource managers and planners with maps that reliably inform about areas vulnerable to hydrological risks, including areas with ephemeral to intermittent flows. This paper demonstrates that the newly developed Wet-Areas Mapping (WAM) process using LiDAR-based point cloud data addresses some of these needs. This is done by portraying local flow patterns, soil drainage, soil moisture regimes and natural vegetation type across mapped areas in a numerically robust and consistent manner. As a result, WAM-derived maps are useful for surprise-free operations planning in several areas of natural resource planning (forestry, parks and recreation, oil and gas extraction, land reclamation), and also serve as field guides for locating and delineating flow channels, road-stream crossings, wet areas and wetlands.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it