A Prototype System for Real-Time Monitoring of Arctic Char in Indoor Aquaculture Operations: Possibilities & Challenges
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
In this exploratory study, we studied and qualitatively evaluated a prototype video data collection system to capture and analyze fish behavior in a small-scale indoor aquaculture operation. The research objective was to design and develop a hardware / software system that would have the potential to capture meaningful data from which to extract fish size, swim trajectory, and swim velocity, ultimately as information toward an assessment of fish health. The initial work presented in this paper discusses the development choices of the prototype system, including various combinations of lighting and camera positions both inside and outside of the aquaculture tanks, and several post-processing techniques to isolate fish in video, calibrate the distance from camera to fish through water, and infer fish trajectories and swim velocities. Preliminary results provided a qualitative assessment of such a system. Specific results on the system's ability to detect fishes' positions, trajectories, and velocities are presently limited to observational outcomes and descriptive statistics rather than large-scale quantitative analysis. The present work lays a foundation for a future commercially hardened system that would be required for the collection of larger datasets, which would in turn facilitate the future development of machine learning (ML) algorithms to begin to statistically correlate data to fish conditions and behaviors in near-real time.
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 | 0.001 | 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