An Optical Imaging System for Capturing Images in Low-Light Aquatic Habitats Using Only Ambient Light
Bibliographic record
Abstract
It is preferable that methods for monitoring fish behavior, diversity, and abundance be noninvasive to avoid potential bias. Optical imaging facilitates the noninvasive monitoring of underwater environments and is best conducted without the use of artificial lighting. Here, we describe a custom-designed optical imaging system that utilizes a consumer-grade camera to capture images in situ in ambient light. This diver-deployed system can be used to collect time series of occurrences of animals while concurrently obtaining behavioral observations for two weeks to a month (depending on the sampling rate). It has also been configured to be paired with a passive acoustic system to record time-synchronized image and acoustic data. The system was deployed in a protected kelp forest off southern California and captured >1,500 high-quality images per day over 14 days. The images revealed numerous fish species exhibiting biologically important behaviors as well as daily patterns of presence/absence. The optical imaging system is a cost-effective tool that can be easily fabricated and improves upon many of the limitations of previous systems, including deployment length and image quality in low-light and limited-visibility conditions. The system provides a relatively noninvasive way to monitor shallow marine habitats, including protected areas, and can augment traditional survey methods by providing nearly continuous observations and thus yield increased statistical power.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".