The community of marine alveolate parasites in the Atlantic inflow to the Arctic Ocean is structured by season, depth, and water mass
Bibliographic record
Abstract
The marine alveolates (MALVs) are a highly diverse group of parasitic dinoflagellates, which may regulate populations of a wide range of hosts, including other dinoflagellates, copepods, and fish eggs. Knowledge on their distribution and ecological role is still limited, as they are difficult to study with morphological methods. In this work, we describe the taxonomic composition and seasonal and depth distribution of MALVs in the Arctic Ocean west and north of Svalbard, based on 18S V4 rRNA metabarcoding data from five cruises. We recovered amplicon sequence variants (ASVs) representing all major groups previously described from environmental sequencing studies (Dino-Groups I–V), with Dino-Groups I and II being the most diverse. The community was structured by season, depth, and water mass. In the epipelagic zone, the taxonomic composition varied strongly by season; however, there was also a difference between Arctic and Atlantic water masses in winter. The spring and summer epipelagic communities were characterized by a few dominating ASVs present in low proportions during winter and in mesopelagic summer samples, suggesting that they proliferate under certain conditions, e.g., when specific hosts are abundant. Mesopelagic samples were more similar across months, and may harbor parasites of deep-dwelling organisms, little affected by season.
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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 | 0.000 | 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".