Quantifying Topographic Characteristics of Wetlandscapes
Bibliographic record
Abstract
Topography underpins natural processes ranging from incident solar radiation to overland flow and water pooling. Despite the influence of topography on natural processes, especially in wetland ecosystems reliant on uplands for water inputs, topography has not been adequately incorporated into reclamation planning. Instead, wetland reclamation projects are typically guided by height-to-length ratios that produce little resemblance to natural wetlands. We present a methodology to quantify the topographic characteristics in landscapes with an abundance of wetlands to guide the reclamation of naturally appearing and self-sustaining wetlandscapes. Topographic characteristics in over 3000 sample landscapes were quantified using terrain roughness and landform element composition and configuration. A large set of metrics was reduced to a statistically independent subset that was applied and compared across three natural regions and a gradient of disturbance. Our results showed that surface roughness and landform element patterns significantly differ among natural regions and that high disturbance landscapes significantly differ from other disturbance levels. To ensure reclaimed wetlandscapes are naturally appearing and self-sustaining, they should replicate the topographic characteristics found within the distribution of surrounding natural landscapes by applying topographic characteristic benchmarks to reclamation design. The presented methodology can be used as a step towards achieving this goal.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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; both teacher heads agree on what is shown here.
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".