Assimilation of carbon and nitrogen by microbial mats from alkaline soda lakes
Bibliographic record
Abstract
Background:Soda lakes are extreme terrestrial ecosystems characterized by high pH, alkalinity, and sodium carbonate concentration. Despite the extreme environment, soda lakes host diverse microbial communities with high primary productivity, carried out by fast-growing phototrophic microbes such as cyanobacteria. In Goodenough Lake, a soda lake on the Cariboo Plateau in BC Canada, carbon isotope analysis indicated that the photosynthetic rate but not bicarbonate availability controlled carbon dioxide assimilation. However, the roles of individual cyanobacteria populations in carbon fixation remain unknown. Despite the rapid growth of microbial mat communities, common nitrogen sources, ammonium and nitrate, were detected only occasionally and in trace amounts in lake water. Mat communities may use alternative nitrogen sources like urea and dinitrogen gas as enzymes for urea assimilation and dinitrogen fixation were highly expressed.Objective:The objective is to measure carbon and nitrogen assimilation by microbial populations in mat communities.Approaches:Incubation of microbial mats from Goodenough Lake with heavy stable isotope labelled bicarbonate, and nitrogen sources, followed by isotope ratio mass spectrometry and proteomics.Results and conclusions:Over 90 different microbial populations were detected in microbial mat communities using proteomics. The sampled mat microbial communities were different from each other, even if samples were close together, but the most abundant populations were the same across samples. The two most abundant cyanobacterial populations exhibited different carbon fixation dynamics, and their abundance was negatively correlated, suggesting that they occupy different ecological niches. Among nitrogen sources, urea was consumed at the highest rate, followed by ammonia. The nitrate consumption rate was much lower, and the fixation of nitrogen was not detected. Urea was consumed mainly during the day. Rates for nitrate and ammonia consumption were similar during the day and the night.
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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".