On Turbulence Production by Swimming Marine Organisms in the Open Ocean and Coastal Waters
Bibliographic record
Abstract
Abstract Microstructure and acoustic profile time series were collected near Ocean Station P in the eastern subarctic North Pacific and in Saanich Inlet at the south end of Vancouver Island, British Columbia, Canada, to examine production of turbulent dissipation by swimming marine organisms. At Ocean Station P, although a number of zooplankton species are large enough to generate turbulence with Reynolds numbers Re > 1000, biomass densities are typically less than 103 individuals per cubic meter (<0.01% by volume), and turbulent kinetic energy dissipation rates ɛ were better correlated with 16-m vertical shear than acoustic backscatter layers. In Saanich Inlet, where krill densities are up to 104 individuals per cubic meter (0.1% by volume), no dramatic elevation of dissipation rates ɛ was associated with dusk and dawn vertical migrations of the acoustic backscatter layer. Dissipation rates are a factor of 2 higher [〈ɛ〉 = 1.4 × 10−8 W kg−1, corresponding to buoyancy Re = 〈ɛ〉/(νN 2) ∼ 140] in acoustic backscatter layers than in acoustically quiet waters, regardless of whether they are vertically migrating. The O(1 m) thick turbulence patches have vertical wavenumber spectra for microscale shear commensurate with the Nasmyth model turbulence spectrum. However, the turbulence bursts of O(10−5 W kg−1) proposed to occur in such dense swarms appear to be rare. Thus far, intense turbulent bursts have been found infrequently, even in very dense aggregations O(104 individuals per cubic meter) characteristic of coastal and high-latitude environs. Based on sampling to date, this corresponds to a frequency of occurrence of less than 4%, suggesting that turbulence production by the marine biosphere is not efficient.
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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 | 0.000 | 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".