Appendicularians and marine snow<i>in situ</i>vertical distribution in Argentinean Patagonia
Bibliographic record
Abstract
Abstract Detailed in situ vertical and temporal distribution of appendicularians, marine snow, fecal pellets, nano- and microplankton were recorded simultaneously with environmental data in the San Jorge Gulf, Argentinean Patagonia (45°–47°S). Data were taken at a fixed station over 36 h in February 2014 with an autonomous Video Plankton Recorder and a FlowCAM®. The water column was thermally stratified with a pycnocline at ~ 40 m. Appendicularians dominated in the upper 65 m with a condensed pattern above the pycnocline at high chlorophyll a concentrations, matching the subsurface chlorophyll maximum layer at ~ 20 m. Our results suggest the absence of vertical migration of appendicularians. Marine snow, strongly correlated with appendicularians, showed high concentrations above the pycnocline, whereas fecal pellets from krill were distributed throughout the water column. Discarded houses of appendicularians or their mucus fragments were the main components of marine snow aggregates, with phytoplankton, detritus and krill pellets also contributing. Nanoplankton dominated over microplankton, with vertical distribution patterns that might depend on local grazing pressure and advective processes. Our study, the first one in the region using underwater imagery, emphasizes the leading contribution of appendicularians to marine snow aggregates in the San Jorge Gulf and their potential implications in the bentho-pelagic coupling.
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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) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".