Distribution and Diversity of a Protist Predator<i>Cryothecomonas</i>(Cercozoa) in Arctic Marine Waters
Bibliographic record
Abstract
Heterotrophic nanoflagellates (HNFs) are key components in microbial food webs, potentially influencing community composition via top-down control of their favored prey or host. Marine cercozoan Cryothecomonas species are parasitoid and predatory HNFs that have been reported from ice, sediments, and the water column. Although Cryothecomonas is frequently reported from Arctic and subarctic seas, factors determining its occurrence are not known. We investigated the temporal and geographic distribution of Cryothecomonas in Canadian Arctic seas during the summer and autumn periods from 2006 to 2010. We developed a Cryothecomonas-specific fluorescent in situ hybridization (FISH) probe targeting ribosomal 18S rRNA to estimate cell concentrations in natural and manipulated samples. Comparison of simple and partial correlation coefficients showed that salinity, depth, and overall community biomass are important factors determining Cryothecomonas abundance. We found no evidence of parasitism in our samples. Hybridized cells included individuals smaller than any formally described Cryothecomonas, suggesting the presence of novel taxa or unknown life stages in this genus. A positive relationship between Cryothecomonas abundance and ice and meltwater suggests that it is a sensitive indicator of ice melt in Arctic water columns.
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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".