Water column interleaving: A new physical mechanism determining protist communities and bacterial states
Bibliographic record
Abstract
During a spring‐summer bloom in a large Arctic polynya, vertically distinct protist communities (phytoplankton and protozoa) occurred within layers caused by interleaving and entrainment of different water masses. We developed a ternary community distance index to quantify the variability in protist community structure in these heterogeneous surface layers. This index was highly correlated (r = 0.984, n = 6) with the extent of physical interleaving (quantified as the root mean square deviations in temperature between measured and smoothed profiles) indicating a high degree of physical‐biotic coupling within water columns of the polynya. Water mass layering created favorable conditions for ciliate blooms. These blooms were associated with distinct temperature‐salinity layers. These layers might act as processing traps for particulate organic matter, with highest concentrations of viruses and bacteria (specifically cells with open or leaky membranes, suggesting microbial grazing pressure) occurring in highly interleaved water columns. In contrast, viral and bacterial concentrations were lowest at a noninterleaved station where protist biomass was dominated by flagellates and small dinoflagellates that were evenly distributed down the water column. Water mass interleaving is likely to contribute to microbial biodiversity, community structure and vertical segmentation of biogeochemical processes in the upper ocean.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".