Particle size distribution at Ocean Station Papa from nanometers to millimeters constrained with intercomparison of seven methods
Bibliographic record
Abstract
Particle size distribution (PSD) is a fundamental property that affects almost every aspect of the marine ecosystem, including ecological trophic interactions and transport of organic matter and trace elements. We measured PSDs using a suite of seven instruments in waters near Ocean Station Papa in the Northeast Pacific Ocean. These instruments and their sizing ranges are: Laser In-Situ Scattering and Transmissometer (LISST)-Volume Scattering Function meter (VSF) and Multispectral Volume Scattering Meter (MVSM), both sizing particles from 0.02 µm to 2000 µm; the LISST-100X, from 3 µm to 180 µm; the ViewSizer, from 0.3 µm to 2 µm; the Coulter Counter, from 2 µm to 40 µm; the Imaging Flow CytoBot (IFCB), from 5 µm to 100 μm; and the underwater vision profiler (UVP), from 100 µm to 2000 µm. Together, they cover an unprecedented size range spanning 5 orders of magnitude from 20 nm to 2 mm. The differences in size definition for the different instruments cause biases in comparing PSDs. The absolute differences in PSDs, after correcting for mean biases, were less than a factor of 3 among all the instruments, and within 50% among LISST-100X, LISST+MVSM, Coulter Counter and IFCB. We also found that particles of sizes <50 µm were not very porous; however, porosity must be considered for particles >50 µm. The merged PSDs, ranging from 0.02 µm to 2000 µm, showed little variation in the PSD slope in the upper 75 m of the water column even though the total number of particles decreased with depth. While submicrometer particles are numerically dominant, particles of sizes 1 µm to 100 µm account for 70–90% of the solid volume of particles. We expect that the results of this study will lead to improved estimates of mass and carbon flux in the study area.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".