Metagenomic survey of the taxonomic and functional microbial communities of seawater and sea ice from the Canadian Arctic
Bibliographic record
Abstract
Climate change has resulted in an accelerated decline of Arctic sea ice since 2001 resulting in primary production increases and prolongation of the ice-free season within the Northwest Passage. The taxonomic and functional microbial community composition of the seawater and sea ice of the Canadian Arctic is not very well known. Bacterial communities from the bottom layer of sea ice cores and surface water from 23 locations around Cornwallis Island, NU, Canada, were extensively screened. The bacterial 16S rRNA gene was sequenced for all samples while shotgun metagenomics was performed on selected samples. Bacterial community composition showed large variation throughout the sampling area both for sea ice and seawater. Seawater and sea ice samples harbored significantly distinct microbial communities, both at different taxonomic levels and at the functional level. A key difference between the two sample types was the dominance of algae in sea ice samples, as visualized by the higher relative abundance of algae and photosynthesis-related genes in the metagenomic datasets and the higher chl a concentrations. The relative abundance of various OTUs and functional genes were significantly correlated with multiple environmental parameters, highlighting many potential environmental drivers and ecological strategies.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".