Bacterial dynamics in first year sea ice and underlying seawater of Saroma-ko Lagoon (Sea of Okhotsk, Japan) and Resolute Passage (High Canadian Arctic): Inhibitory effects of ice algae on bacterial dynamics
Bibliographic record
Abstract
The seasonal development of bacterial abundance in first year bottom ice and underlying seawater were studied at Saroma-ko Lagoon in Hokkaido, Japan, and at Resolute Passage in the High Canadian Arctic during the algal bloom in spring 1992. The aim of this study was to evaluate whether the high algal concentrations reached during the bloom of ice algae have inhibitory effects on bacterial dynamics. Bacterial abundance (measured as total cell count and colony-forming units CFU) increased with the increase of the algal biomass up to 500 micrograms Chla.L-1 in both locations. Culturable fraction (measured as the percentage of CFU counts versus the total cell counts) was between 7% and 22% at Saroma-ko, and approximately 0.08% at Resolute Passage. When algal biomass exceeded 500 micrograms of Chla.L-1, both bacterial abundance and culturable fraction decreased significantly. There was a maximum threshold of algal biomass (between 500 and 800 micrograms of Chla.L-1) after which bacterial dynamics become negatively coupled to the algal biomass. These results suggest that bactericidal and/or bacteriostatic compounds from these extremely high algal concentrations could explain the decrease in bacterial abundance and culturability in bottom ice observed after the ice algae bloom.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".