Underwater infrared video system for behavioral studies in lakes
Bibliographic record
Abstract
We propose the use of infrared (IR) video systems for the underwater behavioral study of animals at night, at depth, and/or in colored aquatic systems. This system is composed of a black‐and‐white video camera coupled with four projectors, each equipped with 50 IR light‐emitting diodes (LEDs). The use of LEDs is energetically far more efficient than the use of halogen lights equipped with IR filters, and therefore allows automatic long‐term video recording at low cost. Laboratory testing of one LED spot at increasing humic acid concentrations indicates that this system can be used for short‐range underwater observation in colored lakes. We applied this system to the study of scavenger/carcass interactions. Under field conditions, the setup is not easily detected by animals and allows direct and continuous observation of animal interactions during a full daily cycle. Note that, to our knowledge, this video system provides the first published night movies of freshwater fish behavior filmed in highly colored lakes, without using visible light. Our results indicate that this nonintrusive system can be successfully used for scientific studies in freshwater ecology, even in the presence of significant concentrations of light‐absorbing humic substances in the water.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".