High-frequency broadband acoustic backscatter from phytoplankton
Bibliographic record
Abstract
Current methods in phytoplankton detection and monitoring are often limited by low temporal and spatial resolution. In principle, the use of a high-frequency broadband acoustic system would be advantageous when used in conjunction with current methods; providing improvements both temporally and spatially. With this motivation, a high-frequency broadband active acoustic system has been developed and used in four separate trials to measure the backscatter from four morphologically-distinct species of phytoplankton. The morphologies studied include (1) a siliceous shelled cylinder, (2) a chain-forming siliceous shell cylinder, (3) a fluid-like spheroid, and (4) a soft-shelled spheroid; and whose sizes range from 10 to 60 µm. Organism cultures were insonified at frequencies between 0.75 MHz and 6.9 MHz giving a ka study range of 0.03–1.73. Volume scattering strength as it varies with ka is presented for each species and compared to potential scattering models drawn from the zooplankton scattering literature. Modifications to the models or model parameters are discussed. Additionally, volume scattering strengths at multiple phytoplankton concentrations are presented and compared to both chlorophyll-a estimates obtained from fluorometers and densities found via flow cytometry. The potential for a phytoplankton species-detection and monitoring system is discussed and evaluated.
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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 | 0.000 | 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) | 0.001 | 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".