Looking for love under the ice: Using passive acoustics to detect burbot (<i>Lota lota</i>: Gadidae) spawning activity
Bibliographic record
Abstract
Abstract Burbot ( Lota lota : Gadidae) is a difficult species to manage effectively due to its preference for deep‐water habitats and under‐ice spawning behaviour, resulting in a poor understanding of its reproductive activity. However, the use of acoustic signalling by burbot as part of their mating system has recently been described and this behaviour may provide a means of investigating questions regarding the spatial and temporal distribution of spawning aggregations using passive acoustic monitoring. We used audio and video recording to confirm that burbot vocalise and that these vocalisations can be detected under field conditions as well as to characterise the relationship between burbot acoustic signalling and spawning behaviour. We also evaluated the feasibility of locating and monitoring burbot spawning aggregations in real time using passive acoustics. Burbot vocalisations were difficult to identify with only about 6% of the recordings containing calls being successfully identified as such in the field. Burbot vocalised more often between sundown and sunrise than during daylight hours. Calls recorded at night tended to be lower frequency, longer duration, and have lower bandwidth than those made during the day. Burbot vocalisations could not be recorded in conjunction with video recordings of spawning activity, indicating that burbot may not call during active spawning, but may use acoustic communication to signal the onset of reproductive readiness and to form pre‐spawning aggregations. While burbot calls were readily identifiable, observers had a difficult time identifying burbot calls in real time under field conditions. Passive acoustic monitoring demonstrates considerable potential as a management tool to locate burbot spawning grounds and identify periods of activity, but may not be an appropriate technique for monitoring spawning activity in real time.
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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 | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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; both teacher heads agree on what is shown here.
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".