Quantitative visualization of geophysical flows using low-cost oblique digital time-lapse imaging
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Estuaries and coastal waters are regions where many different important physical processes can be found. Although the physical scale of these processes is often relatively small, their time scales are correspondingly rapid and aliasing is usually a problem in sampling programs. There can be significant spatial variations in mixing and flow patterns, which are usually learned only through long experience in a particular region. Observational and interpretation difficulties might be significantly simplified with a simple remote sensing tool to be used in conjunction with standard techniques. Here, the use of digital time-lapse photography at highly oblique angles as a tool for flow visualization is discussed. The interaction of surface waves with slicks and internal motions can cause apparent changes in the shade and color of water at shallow angles in a way not apparent in downlooking views. The use of time-lapse techniques allows us to isolate time scales of interest and, by "speeding up" low-frequency motions, causes them to become more apparent to the eye. A cheap and portable system based on commercially available equipment is described and various advantages and shortcomings are discussed. Results are shown to illustrate the utility of the observational system.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it