Development of an Inexpensive Automated Streamflow Monitoring System
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
Informative/Abstract:Estimating streamflow is time and labour intensive due to the necessity of developing a rating curve. The development of a rating curve involves acquiring at least thirty in-field measurements of streamflow across a wide range of flow levels, which can be costly and impractical in remote regions with limited seasonal access. Here we showcase an automated system which accurately estimates streamflow multiple times each day, greatly facilitating the development of rating curves for remote or seasonally inaccessible sites. The system uses an emerging technique referred to as particle image velocimetry (PIV) to track the movement of objects and flow structure features on the mobile water surface to generate velocity vector grids. Velocity grids were used to calculate streamflow and facilitate the development of a rating curve. This represents the first use of an automated PIV system to estimate streamflow in small streams (< 5 m wide) and the first system to automatically distribute particles for facilitated PIV analysis.Keywords: Particle Image Velocimetry, Streamflow Monitoring, Automated Systems, Particle TracerFunding: This research was funded through the Ministry of Natural Resources and Forestry, the Canada-Ontario Agreement Fund, and the Queen Elizabeth II Graduate Scholarship in Science and Technology.
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 | 0.001 |
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