Morphologic characterization of upland depressional wetlands on the Des Moines Lobe of Iowa
Bibliographic record
Abstract
An algorithm developed to identify, delineate, and derive the morphology of drained depressional features within a landscape was applied to the Iowa portion of the Des Moines Lobe (DML-IA) geomorphic sub-region of the Prairie Pothole Region of North America (PPR), using high resolution LiDAR derived Digital Elevation Models (DEMs). Nearly 240,000 unique upland depressions were identified and their individual morphologies determined. Testing of our algorithm against an algorithm designed to integrate over triangulated surface representations of 975 randomly selected depressions from the DML-IA dataset reveals that our computational process produces morphology results to within 0.3 and 2% of those obtained using the latter process, and is nearly 3 orders of magnitude faster. Maximum areas of inundation, maximum depths, and maximum storage volumes were determined to follow a power-law distribution. Maximum volume was determined to be strongly related to maximum area through a power-law model, the coefficients of which appear to vary significantly from other areas of the PPR, but are in close agreement with values obtained for small sub-areas of the DML-IA, and for a large river basin in North Saskatchewan, CN. While the majority (80%) of depressions within the DML-IA are less than 1 ha in area, these only comprise 9.8% of the total potential depressional storage and 25.6% of the total depressional wetland area of this landscape. More than half of the potential storage capacity is provided by depressions between 1 and 30 ha.
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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 | 0.000 |
| 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 | 0.000 |
| 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".