A Velocity Meter for Quantifying Advection Velocity Vectors in Large Water Bodies
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
A velocity meter was designed and built in order to meet market needs for an affordable instrument that measures the range of velocity magnitudes and direction experienced in medium- to large-sized water bodies. The velocity meter consists of a graduated plate with an injector protruding from the center and a camera held downward above the plate. Once the Dye Injection Velocity (DIV) meter is in the flow, dye is injected and the camera records the dye fluid transport. The recorded video is analyzed to determine the local flow velocity and direction. The DIV was calibrated for a range of velocities between 0.0094 m/s and 0.1566 m/s using particle image velocimetry (PIV) in a flow visualization flume. The accuracy of the instrument was found to be +6.3% and −9.8% of full scale. The coefficient of determination of the calibration curve was equal to 98%. Once calibrated, the DIV was deployed to the Inverness Stormwater pond in Calgary, Canada, for validation tests against an Acoustic Doppler Velocity (ADV) meter. During the validation tests, both flow velocity magnitude and direction were measured at several spatial points. The velocity magnitude results showed good agreement and the Mann-Whitney test showed no statistically significant difference (p-value > 0.05). At two spatial points, the differences between direction data were significant, which could be caused by the random errors involved in the validation test. However, the averaged data showed good agreement.
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 | 0.000 |
| 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 it