Groundtruthing of pelagic forage fish detected by hydroacoustics in a whale feeding area using environmental DNA
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Conservation of whales, considered as umbrella species in marine environments, requires to be able to understand their relationships with ecosystem components such as prey species, including pelagic fish. However, studying such relationships in nature is a technical challenge. In this study, we used two noninvasive methods in combination, namely hydroacoustics and environmental DNA (eDNA), to detect five pelagic or semipelagic fish species in the Saguenay–St. Lawrence Marine Park (Québec, Canada): the sandlance Ammodytes sp., the Atlantic herring Clupea harengus , the capelin Mallotus villosus , the rainbow smelt Osmerus mordax, and the redfish Sebastes sp. The Marine Park is a major summer feeding ground for a wide diversity of marine wildlife species, including the endangered St. Lawrence beluga whale population. Up to now, scarce research efforts have been dedicated to the estimation of pelagic fish abundance and diversity in this area. Hydroacoustics allowed to easily discriminate the classification of echoes from fish, and with certain limitations to distinguish swim bladder fish from fish without swim bladder. We used eDNA to groundtruth acoustics data and to improve species identification. eDNA analyses especially demonstrated that the capelin was the most predominant species, while the abundance of the redfish and the sandlance was strongly variable over the 2 years of the study. Our results also suggest that there are annual fluctuations in prey availability that marine mammals encounter in this area. Although the approach we used is not without constraints that should be addressed in future studies, we hope that this study will contribute to science‐based conservation and fisheries management policies.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it