Broadband acoustics on the VENUS observatory in Saanich Inlet.
Bibliographic record
Abstract
High-frequency sonar is by far the most cost-effective way of “profiling” the water column from an ocean observing system. From a biological oceanographic perspective, long-term acoustic observations are rich with information on the depths and abundances of fish and zooplankton. The drawback is that it is difficult to conclusively identify which species (or even functional groups) are present at any given time. This can be done, but only with plenty of supporting data, generally acquired non-autonomously. Broadband acoustics may be the key to making acoustic observations of fish and zooplankton less qualitative. Here we explore this idea. We present nearly two years (Apr. 2008–Feb. 2010) of broadband (85–155 kHz) echosounder data collected on the VENUS observatory in Saanich Inlet. Using historical and contemporaneous (July 30, 2009) zooplankton net-tow data, we attempt to automate and interpret the resulting classification of scattering layers throughout the long-term record.
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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 | 0.000 |
| 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".