Model Sensitivity to Topographic Uncertainty in Meso- and Microtidal Marshes
Bibliographic record
Abstract
Light detection and ranging (Lidar) derived digital elevation models are widely used in modeling coastal marsh systems. However, the topographic error in these models can affect simulations of marsh coverage and characteristics. We investigated the relevance and impact of this error in micro- and mesotidal systems. Lidar-derived and RTK-adjusted topography were each used in a dynamic marsh model, and the resulting marsh coverages were examined. For two microtidal sites (Apalachicola, FL, USA, and Grand Bay, MS, USA) using solely lidar-derived topography, the model produced Cohen Kappa values of 0.1 for both estuaries when compared with National Wetland Inventory data indicating “very poor agreement.” Applying the RTK-adjusted topography improved the model marsh coverage results to “substantial agreement” with the values to 0.6 and 0.77, respectively. The mesotidal site in Plum Island, MA, USA, contained similar topographic errors, but the model produced a Cohen Kappa value of 0.73, which categorized it as “very good agreement” with no need for a further topographic adjustment given its present robust biomass productivity. The results demonstrate that marsh models are sensitive to topographic errors when the errors are comparable to the tidal range. The particular sensitivity of the modeling results to topographic error in microtidal systems highlights the need for close scrutiny of lidar-derived topography.
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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 | 0.001 |
| 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".