A Novel System for Real-Time, In Situ Monitoring of CO2 Sequestration in Photoautotrophic Biofilms
Bibliographic record
Abstract
Climate change brought about by anthropogenic CO2 emissions has created a critical need for effective CO2 management solutions. Microalgae are well suited to contribute to efforts aimed at addressing this challenge, given their ability to rapidly sequester CO2 coupled with the commercial value of their biomass. Recently, microalgal biofilms have garnered significant attention over the more conventional suspended algal growth systems, since they allow for easier and cheaper biomass harvesting, among other key benefits. However, the path to cost-effectiveness and scaling up is hindered by a need for new tools and methodologies which can help evaluate, and in turn optimize, algal biofilm growth. Presented here is a novel system which facilitates the real-time in situ monitoring of algal biofilm CO2 sequestration. Utilizing a CO2-permeable membrane and a tube-within-a-tube design, the CO2 sequestration monitoring system (CSMS) was able to reliably detect slight changes in algal biofilm CO2 uptake brought about by light–dark cycling, light intensity shifts, and varying amounts of phototrophic biomass. This work presents an approach to advance our understanding of carbon flux in algal biofilms, and a base for potentially useful innovations to optimize, and eventually realize, algae biofilm-based CO2 sequestration.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".