Development and deployment of a point‐source digital inline holographic microscope for the study of plankton and particles to a depth of 6000 m
Bibliographic record
Abstract
A point‐source digital inline holographic microscope (DIHM) was designed for the imaging of particles from 50 µm to several millimeters in size. The DIHM operates autonomously without connection to external recording devices or power sources and delivers 4.2 megapixel images at a rate of approximately 7 images s −1 , each image representing a snapshot of 1.8 mL seawater. Reliance on largely off‐the‐shelf components, and simplification in its construction makes this camera system adaptable to various particle size ranges and environments, and easy‐to‐operate for nonexpert users. The DIHM produced sharp images of protists with skeletal structures (e.g., acantharians, tintinnids, dinoflagellates), mesoplankton (e.g., copepods, appendicularians, medusae), Trichodesmium colonies and marine snow particles while descending in the water column at 1 m s −1 , a typical velocity for deployments of tethered instruments and samplers from oceanographic vessels. To validate the usefulness of the new instrument in an oceanographic context, data are presented of the surface distribution of Trichodesmium spp., and of the vertical frequency distribution of fecal pellets and other particles in the deep sea. The point‐source DIHM has the potential to become a standard instrument on the CTD rosette (i.e., on the basic oceanographic instrument and sampling frame) in the future providing a permanent archival record of the water column that can be mined for specific target particles in the future.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".