Long-term broadband acoustic observations of zooplankton scattering layers in Saanich Inlet, British Columbia.
Bibliographic record
Abstract
The application of broadband techniques to fish and zooplankton bioacoustics is showing potential to transform the field into one that is much more quantitative. This is because broadband techniques allow the use of the known spectra of organisms or nonbiological sources of scattering to distinguish between scatterers, allowing discrimination without the need for extensive groundtruthing. This makes it ideal for remote monitoring of fish or zooplankton assemblages, since continuous net-sampling is often not possible. An upward-looking 85–155 kHz broadband sonar has been collecting data nearly continuously on the Victoria Experimental Network Under the Sea (VENUS) mooring in Saanich Inlet, British Columbia since March 2008. Saanich Inlet is known to have large populations of euphausiids, which create a strong acoustic scattering layer that migrates from depth to the surface and back each day. The thickness, timing, strength and spectral response of this layer is examined throughout the annual cycle and the feasibility of using this type of sonar to elucidate changing zooplankton assemblages (due to growth and changing species composition) is assessed.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".