Bibliographic record
Abstract
Climate change caused by the accumulation of CO2 in the atmosphere has emphasized the need for effective CO2 mitigation strategies. Microalgae are fast-growing photoautotrophic microorganisms that use light energy to take up and fix CO2, producing biomass with inherent value and applicability in fields like agriculture, nutrition, and bioenergy. The fact that wastewater can be used to support microalgal growth presents the opportunity to achieve the “triple benefit” of integrated CO2 capture, wastewater remediation, and value-added biomass production. While microalgal cultivation has conventionally utilized monocultures growing in suspension, microalgae in nature largely exhibit sessile growth in mixed-species biofilms with close associations to other microorganisms. Considering one of the tenets of microbial community ecology is that species diversity promotes productivity, the use of mixed, non-axenic phototrophic biofilms in algal biotechnologies can present a new paradigm which harnesses natural phototrophic microbial ecosystems and the ecological and physiological advantages that they offer. The research presented herein set out to expand our understanding of mixed phototrophic biofilms. A key interest was the CO2 uptake performance of these biofilms under conditions that are relevant for the integration of CO2 mitigation, wastewater treatment, and biomass production via photosynthetic growth. A novel CO2 sequestration monitoring system (CSMS) was developed to track real-time CO2 uptake by phototrophic biofilms, which demonstrated good sensitivity in detecting changes in uptake rate brought about by varying environmental and cultivation conditions. It was also shown that the presence and concentration of organic carbon sources significantly impacted biofilm carbon capture and led to observable longitudinal partitioning of heterotrophic and autotrophic growth. The system was further used to evaluate the impact of nitrogen starvation, a common algal biomass optimization strategy, on biofilm CO2 uptake. Starvation appeared to promote sloughing of biofilm biomass and coincided with a steady, near linear decrease in CO2 uptake rate. These insights contribute to an improved understanding of phototrophic biofilms and represent an important step toward large-scale biofilm-based CO2 mitigation.
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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 | 0.000 |
| 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".