A decadal perspective on north water microbial eukaryotes as Arctic Ocean sentinels
Bibliographic record
Abstract
The North Water region, between Greenland and Ellesmere Island, with high populations of marine birds and mammals, is an Arctic icon. Due to climate related changes, seasonal patterns in water column primary production are changing but the implications for the planktonic microbial eukaryote communities that support the ecosystem are unknown. Here we report microbial community phenology in samples collected over 12 years (2005-2018) from July to October and analysed using high throughput 18S rRNA V4 amplicon sequencing. Community composition was tied to seasonality with summer communities more variable than distinct October communities. In summer, sentinel pan-Arctic species, including a diatom in the Chaetoceros socialis-gelidus complex and the picochlorophyte Micromonas polaris dominated phytoplankton and were summer specialists. In autumn, uncultured undescribed open water dinoflagellates were favored, and their ubiquity suggests they are sentinels of arctic autumn conditions. Despite the input of nutrients into surface waters, autumn chlorophyll concentrations remained low, refuting projected scenarios that longer ice-free seasons are synonymous with high autumn production and a diatom dominated bloom. Overall, the summer sentinel microbial taxa are persisting, and a subset oceanic dinoflagellate should be monitored for possible ecosystem shifts as later autumn ice formation becomes prevalent elsewhere.
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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 | 0.002 | 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 | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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 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".