TrapCam: an inexpensive camera system for studying deep‐water animals
Bibliographic record
Abstract
Summary 1. Behavioural research in deep water (>40 m depth) has traditionally been expensive and logistically challenging, particularly because the light and sound produced by underwater vehicles make them unsuitably disruptive. Yet, understanding the behaviour of deep‐water animals, especially those targeted by exploitation, is important for conservation. For example, understanding interactions between animals and deep‐water fishing gear could inform the design of devices that minimize bycatch. 2. We describe the ‘TrapCam’, a self‐contained, high‐definition video system that requires neither the support of a vessel once deployed nor special equipment to deploy or retrieve. This system can record 13‐h videos at 1080p resolution and is deployable on any substrata at depths of up to 100 m. The system is inexpensive (<$3000 USD), versatile and suited to the study of animal behaviour at depths inaccessible to scuba divers. 3. We evaluate the performance and cost effectiveness of TrapCam and analyse videos retrieved from pilot deployments to observe spot prawn ( Pandalus platyceros ) traps at 100 m depth. Preliminary analyses of animal–prawn trap interactions yield novel insights. We provide future directions for researchers to use this type of camera system to study deep water‐dwelling species around the world.
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How this classification was reachedexpand
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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".