Comparison of grid‐based algorithms for computing upslope contributing area
Bibliographic record
Abstract
Terrain attributes based on upslope contributing area, A , are used widely in distributed hydrologic models. Several grid‐based algorithms are available for estimating A . In this study, five algorithms (D8, ρ 8, MFD, DEMON, and D∞) were compared quantitatively on two undulating agricultural fields (63 and 109 ha) in northeastern Colorado. Global positioning system (GPS) data (0.02‐m accuracy) were used to generate grid digital elevation models (DEMs) at 5‐, 10‐, and 30‐m cell sizes. Relative differences between A values estimated using single‐ and multiple‐direction algorithms increased with decreasing grid cell size. Relative differences were greatest along ridges and side slopes, and differences decreased where the terrain became more convergent. Multiple‐direction algorithms (MFD, DEMON, and D∞), allowing for flow divergence, are recommended on these undulating terrains for 5‐ and 10‐m grids where A is most sensitive to the algorithm selection.
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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 | 0.002 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".