Diversity and Distribution of Marine Microbial Eukaryotes in the Arctic Ocean and Adjacent Seas
Bibliographic record
Abstract
We analyzed microbial eukaryote diversity in perennially cold arctic marine waters by using 18S rRNA gene clone libraries. Samples were collected during concurrent oceanographic missions to opposite sides of the Arctic Ocean Basin and encompassed five distinct water masses. Two deep water Arctic Ocean sites and the convergence of the Greenland, Norwegian, and Barents Seas were sampled from 28 August to 2 September 2002. An additional sample was obtained from the Beaufort Sea (Canada) in early October 2002. The ribotypes were diverse, with different communities among sites and between the upper mixed layer and just below the halocline. Eukaryotes from the remote Canada Basin contained new phylotypes belonging to the radiolarian orders Acantharea, Polycystinea, and Taxopodida. A novel group within the photosynthetic stramenopiles was also identified. One sample closest to the interior of the Canada Basin yielded only four major taxa, and all but two of the sequences recovered belonged to the polar diatom Fragilariopsis and a radiolarian. Overall, 42% of the sequences were <98% similar to any sequences in GenBank. Moreover, 15% of these were <95% similar to previously recovered sequences, which is indicative of endemic or undersampled taxa in the North Polar environment. The cold, stable Arctic Ocean is a threatened environment, and climate change could result in significant loss of global microbial biodiversity.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".