Combining <i>in situ</i> and <i>ex situ</i> plankton image data to reconstruct zooplankton (&gt;1 mm) volume and mass distribution in the global ocean
Bibliographic record
Abstract
Abstract Plankton size spectra are important indicators of the ecosystem state, yet such measurements are typically biased by the available sampling methods. Here, we combined individual size measurement from two zooplankton imaging approaches—in situ observation by the Underwater Vision Profiler5 and Multinet-collection supplemented by ex situ imaging via Zooscan—obtained in the global ocean, to calculate zooplankton normalized biovolume size spectra (NBSS) for all organisms larger than 1 mm. The reconstructed NBSS combining both datasets resulted in increased biomass estimates by adding organisms poorly sampled by either of the methods. The optimal values measured by both methods are used to reconstruct the zooplankton biovolume and biomass distributions. The reconstructed slopes appeared steeper and closer to those measured only by the UVP5 (+7.6%) and flatter than those obtained only from the Multinet (−20%), particularly in tropical and temperate latitudes. The main difference in tropical and temperate NBSS from the two devices is due to the fragile rhizarians that were not accounted for when using net data. When possible, we suggest using in situ and ex situ technologies together, and we provide potential indications on how to correct for missing components of the community when only one method is available.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".