BIOKINETIC CHARACTERIZATION OF FERROPLASMA ACIDIPHILUM
Bibliographic record
Abstract
Abstract. Recently a mixed culture dominated by the iron-oxidizing microorganisms Leptospirillum and Ferroplasma has been used in a large-scale microbial fuel cell for electrical power generation. There are many factors that affect the kinetics of iron oxidation by the mixotroph Ferroplasma acidiphilum. This study investigated the effects of pH, temperature, and substrate and yeast extract concentrations in order to arrive at kinetically favorable operating conditions with minimal jarosite precipitation. Furthermore, F. acidiphilum was cultured with Leptospirillum sp. in order to determine its viability as a species capable of limiting the organic by-products of chemolithotrophic microbial growth. Bacterial characterization in the culture was accomplished using fluorescent in situ hybridization (FISH). It was found that the ferrous iron oxidation was most favorable at yeast extract concentration of 0.02% (w/v), pH of 1.6, temperature of 35 oC and an initial ferrous iron concentration of 1 g/L yielding a maximum specific growth rate of 0.0351-0.042 h-1. Moreover, F. acidiphilum displayed a symbiotic relationship with its chemolithotrophic counterpart, Leptospirillum sp. in that they were able to utilize the metabolic organic products of the chemolithotroph and limit the organic concentration to ~20 ppm total organic carbon (TOC), well below the threshold concentration of 250 ppm for chemolithotroph activity.
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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 | 0.001 | 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".