The Behavioral Responses of a Warmwater Teleost to Different Spectra of Light-Emitting Diodes
Bibliographic record
Abstract
Abstract Freshwater ecosystems are threatened by a wide range of anthropogenic infrastructure related to hydropower, irrigation, municipal withdrawals, and industrial cooling. Technology can be used to mitigate the loss of fish associated with such infrastructure by exploiting the sensory physiology of a species through stimuli designed to manipulate their natural behaviors (e.g., to attract or repel). Technologies used for behavioral guidance often incorporate light; however, previous studies investigating light devices have focused on mercury vapor bulbs and thus have been limited in their exploration of the broader light spectra. Innovations in light-emitting diode (LED) technology provide opportunities for manipulating light spectra (i.e., color) as well as light-pulse frequency. We tested the behavioral response of Largemouth Bass Micropterus salmoides under 16 different LED color and light-pulse frequency combinations as well as in a control in which no light was emitted. Red, orange, yellow, and green were considered with four light-pulse frequencies (0, 120, 300, and 600 pulses/min). Using a large shallow arena, lateral fish movement in response to the light treatments was examined. Regardless of color or light-pulse frequency, fish were repelled by the light source. In contrast, when there was no light emitted, fish were evenly distributed throughout the arena. This work suggests that colored light accompanied with light-pulse frequencies produced by LEDs can induce an avoidance response in Largemouth Bass. Received May 7, 2015; accepted January 5, 2016 Published online August 24, 2016
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".