FishCam: A low-cost open source autonomous camera for aquatic research
Bibliographic record
Abstract
500 USD) autonomous camera package to record videos and images underwater. The system is composed of easily accessible components and can be programmed to turn ON and OFF on customizable schedules. Its 8-megapixel camera module is capable of taking 3280 × 2464-pixel images and videos. An optional buzzer circuit inside the pressure housing allows synchronization of the video data from the FishCam with passive acoustic recorders. Ten FishCam deployments were performed along the east coast of Vancouver Island, British Columbia, Canada, from January to December 2019. Field tests demonstrate that the proposed system can record up to 212 h of video data over a period of at least 14 days. The FishCam data collected allowed us to identify fish species and observe species interactions and behaviors. The FishCam is an operational, easily-reproduced and inexpensive camera system that can help expand both the temporal and spatial coverage of underwater observations in ecological research. With its low cost and simple design, it has the potential to be integrated into educational and citizen science projects, and to facilitate learning the basics of electronics and programming.
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.039 | 0.010 |
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".