Assessing plankton and other particles in situ with the SOLOPC
Bibliographic record
Abstract
We combined a Sounding Oceanographic Lagrangian Observer float with a Laser Optical Plankton Counter (LOPC) and a fluorometer to make an autonomous biological profiler, the SOLOPC. The instrument senses plankton and other particles over a size range of 100 µm to 1 cm in profiles to 300 m in depth and sends data ashore via satellite. Objects sensed by the LOPC include aggregates and zooplankton, the larger of which can be distinguished from one another by their transparency. We hypothesized that the diel production of particles and their loss by sinking and grazing are reflected in the change of the particle distribution. We present data from four deployments of the SOLOPC off California. Particle volume was maximal at the base of the surface mixed layer and correlated with chlorophyll a fluorescence. In a 3‐d deployment in 2005, particle volume was greatest in the early evening and smallest in the morning, and average particle size increased with depth. Eigenvector analysis of the particle volume distribution as a function of diameter for each of the deployments yielded size peaks characteristic of planktonic crustaceans. Ship‐based measurements showed that the abundance of opaque particles of 1.1‐1.7 mm equivalent spherical diameter was positively correlated with copepods of this size and simultaneously collected in nets. This relationship was used with SOLOPC data to estimate the distribution of large copepods, which were most abundant beneath the depth of maximal particle flux, estimated from particle size and published sinking rates. Our data are consistent with a model with diel production of particles and their loss by sinking and grazing.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".