Identifying zooplankton fecal pellets from <i>in situ</i> images
Bibliographic record
Abstract
Abstract Zooplankton play a crucial role in the biological carbon pump by producing sinking particles including sloppy feeding by-products, fecal pellets, molts and carcasses. However, quantifying their impact of these particles on the carbon cycle remains difficult. The contribution of fecal pellets to particulate organic carbon export is usually assessed using fecal pellets collected from sediment traps and laboratory studies. Here, we identified 50 771 fecal pellet-like particles distributed across three morphological clusters. These were extracted from 987 236 in situ images of non-living particles collected from Baffin Bay (Arctic Ocean) using the Underwater Vision Profiler (UVP). We associated which taxonomic groups produced the fecal pellets by comparing the UVP images with observations of fecal pellet morphology and length. Our results emphasize the feasibility of quantifying fecal pellets from in situ images and the importance of developing the resolution of imaging tools that would simultaneously identify smaller fecal pellet-like particles and capture images of large crustacean zooplankton. Using in situ images in identifying fecal pellets will facilitate a better understanding of their dynamics, a more accurate calculation of carbon fluxes, and the representation of fecal pellets in biogeochemical models.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".