Bacterioplankton seasonal dynamics and their controlling factors
Bibliographic record
Abstract
Bacteria are a very important component of the microbial food-web in the ocean. The efficiency of the microbial food-web influences the biogeochemical cycles of various elements. Thus the activity of the bacterial assemblage in the water column can substantially affect the biogeochemistry of the water. Primary production and its products have been suggested to be the major food source for bacterial growth. Furthermore, temperature is frequently reported to limit bacterial growth interactively with substrate availability. But there has also been some inconsistency in reports and further research is needed. Thus, the seasonal variation of bacterial abundance, biomass, production and community structure was investigated from November 2004 until September 2005 in Logy Bay, Newfoundland. In addition, growth experiments were carried out to characterise the response of the bacterial community to differing temperatures and substrate concentrations. Logy Bay is a coastal, cold-temperate to subpolar environment with water temperatures ranging from - -1.5° C to 16° C during the year. The bay is oligotrophic with a short spring phytoplankton bloom. The productivity of the bacterial assemblage appeared to be bottom-up controlled by dissolved organic matter released during primary production. Besides substrate control of bacterial activity, in situ temperature limited the production rates. Bacterial biomass was most likely top-down controlled by bacterivorous grazers. Coincident with the phytoplankton, the bacterial community changed suggesting that different bacterial groups are more competitive at high or low substrate concentrations. Besides the concentration, the composition of the ambient dissolved organic matter might be relevant for bacterial community structure changes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".