Acoustic estimates of zooplankton biomass and distribution: application of canonical correlation to scaling of multifrequency acoustic data
Bibliographic record
Abstract
A technique is presented that can be used to scale multifrequency acoustic data to estimates of wet weight biomass from concurrently collected zooplankton samples. The technique uses canonical correlation to identify those taxa that correlate with the acoustic data. Canoncial correlation is then used to compute scaling coefficients that can be used to convert transect data to biomass equivalents for the correlating taxa. Examples are offered to illustrate how the technique can identify euphausiid aggregates, pteropods, and larval fish. The technique can discriminate between taxa with gas bladders, taxa with shells, and fluid-filled taxa. It also has the advantage of quantifying the correlation between the target taxa and acoustic data, thus allowing the researcher to judge the usefulness of the acoustic data in characterizing the distribution and density of the zooplankton taxa.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".