Additional file 1 of Autofermentation of alkaline cyanobacterial biomass to enable biorefinery approach
Bibliographic record
Abstract
Additional file 1: Figure S1. Change in pressure and accumulated methane concentration obtained from inoculation of untreated and treated highly alkaline and high pH microalgal biomass with activated sewage sludge inoculum during 30 and 40 days of incubation respectively. Methane concentration was detected with gas chromatography at different time points of the incubation. Experiments were performed in triplicates and statistical analysis were done with two-way Anova test with a significance level of 0.05. Figure S2. Change in biomass concentration by natural settling for 6 h (a) and centrifugation at different speed for 15 min (b) and water recovery efficiency. Figure S3. The effect of fermentation temperature on soluble protein and sugar (mg per g-initial biomass) during 10 days of anoxic dark fermentation. Initial pH in all cases was 10.36 ± 0.05. Values reported correspond to the average of triplicate measurements with 95% confidence interval. Figure S4. The effect of harvesting and dewatering method on soluble protein and sugar (mg per g-initial biomass) during 10 days of anoxic dark fermentation. Initial pH in all cases was 10.48 ± 0.02. Values reported correspond to the average of triplicate measurements with 95% confidence interval. Table S1. Carbon balance for the autofermentation of highly alkaline cyanobacterial biomass at different initial biomass concentrations. Carbon distribution results are reported after 8 days under dark anaerobic conditions. Table S2. Carbon balance for the autofermentation of highly alkaline cyanobacterial biomass under hypoxic and anoxic conditions at 21 °C. Carbon distribution results under hypoxic and anoxic are reported after 4 and 8 days respectively.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.839 | 0.185 |
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".