Characterization of aeolian streamers using time-average videography
Bibliographic record
Abstract
Aeolian streamers are common in prototype saltation systems. Streamers are elongate, flow-aligned features within which the concentrations of saltating grains are large relative to a spanwise average concentration. The occurrence of streamers introduces substantial spatial and temporal variability in local sand transport rates. There have been few studies to attempt to characterize the scales of streamers, and the results of those studies have been constrained because they use Eulerian approaches to measure an inherently Lagrangian process. We describe the results from a field experiment designed to address this methodological problem. Field experiments were conducted at Jericoacoara, Ceará, Brazil, in October, 2011. The wind field was measured with ultrasonic anemometers and ruggedized thermal probes. Transport rates were measured using Miniphones, Wenglor Particle Counters, and hose traps. A set of three video cameras, deployed in a triangular array, was used to capture images of streamers. The field of vision for the central, upwind-facing camera was a minimum of about 15 m, expanding to more than 100 m in the middle distance. Video images were time-averaged over a number of intervals, ranging from 1-64 seconds, to establish characteristic path lengths and spatial and temporal scales. The results of these analyses show that this methodological approach is technically sound. Streamer characteristics are center-to-center spacings of about 1 m, length-scales exceeding 50 m and time scales of individual streamers exceeding 64 s.
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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 | 0.002 | 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) | 0.001 | 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".