Predictability of dune activity in real dune fields under unidirectional wind regimes
Bibliographic record
Abstract
Abstract We present an analysis of 10 dune fields to test a model‐derived hypothesis of dune field activity. The hypothesis suggests that a quantifiable threshold exists for stabilization in unidirectional wind regimes: active dunes have slipface deposition rates that exceed the vegetation deposition tolerance, and stabilizing dunes have the opposite. We quantified aeolian sand flux, slipface geometry, and vegetation deposition tolerance to directly test the hypothesis at four dune fields (Bigstick, White Sands Stable, White Sands Active, and Cape Cod). We indirectly tested the hypothesis at six additional dune fields with limited vegetation data (Hanford, Año Nuevo, Skagen Odde, Salton Sea, Oceano Stable, and Oceano Active, “inverse calculation sites”). We used digital topographic data and estimates of aeolian sand flux to approximate the slipface deposition rates prior to stabilization. Results revealed a distinct, quantifiable, and consistent pattern despite diverse environmental conditions: the modal peak of prestabilization slipface deposition rates was 80% of the vegetation deposition tolerance at stabilized or stabilizing dune fields. Results from inverse calculation sites indicate deposition rates at stabilized sites were near a hypothesized maximum vegetation deposition tolerance (1 m a −1 ), and active sites had slipface deposition rates much higher. Overall, these results confirm the hypothesis and provide evidence of a globally applicable, simple, and previously unidentified predictor for the dynamics of vegetation cover in dune fields under unidirectional wind regimes.
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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 | 0.002 |
| 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 | 0.001 | 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 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".