Interaction between CO2-consuming autotrophy and CO2-producing heterotrophy in non-axenic phototrophic biofilms
Bibliographic record
Abstract
As the effects of climate change become increasingly evident, the need for effective CO2 management is clear. Microalgae are well-suited for CO2 sequestration, given their ability to rapidly uptake and fix CO2. They also readily assimilate inorganic nutrients and produce a biomass with inherent commercial value, leading to a paradigm in which CO2-sequestration, enhanced wastewater treatment, and biomass generation could be effectively combined. Natural non-axenic phototrophic cultures comprising both autotrophic and heterotrophic fractions are particularly attractive in this endeavour, given their increased robustness and innate O2-CO2 exchange. In this study, the interplay between CO2-consuming autotrophy and CO2-producing heterotrophy in a non-axenic phototrophic biofilm was examined. When the biofilm was cultivated under autotrophic conditions (i.e. no organic carbon), it grew autotrophically and exhibited CO2 uptake. After amending its growth medium with organic carbon (0.25 g/L glucose and 0.28 g/L sodium acetate), the biofilm rapidly toggled from net-autotrophic to net-heterotrophic growth, reaching a CO2 production rate of 60 μmol/h after 31 hours. When the organic carbon sources were provided at a lower concentration (0.125 g/L glucose and 0.14 g/L sodium acetate), the biofilm exhibited distinct, longitudinally discrete regions of heterotrophic and autotrophic metabolism in the proximal and distal halves of the biofilm respectively, within 4 hours of carbon amendment. Interestingly, this upstream and downstream partitioning of heterotrophic and autotrophic metabolism appeared to be reversible, as the position of these regions began to flip once the direction of medium flow (and hence nutrient availability) was reversed. The insight generated here can inform new and important research questions and contribute to efforts aimed at scaling and industrializing algal growth systems, where the ability to understand, predict, and optimize biofilm growth and activity is critical.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".