Energy use efficiency of soil microorganisms: Driven by carbon recycling and reduction
Bibliographic record
Abstract
Abstract Carbon use efficiency (CUE) is being intensively applied to quantify carbon (C) cycling processes from microbial cell to global scales. Energy use efficiency (EUE) is at least as important as the CUE because (i) microorganisms use organic C mainly as an energy source and not as elemental C per se, and (ii) microbial growth and maintenance are limited by energy, but not by C as a structural element. We conceptualize and review the importance of EUE by soil microorganisms and focus on (i) the energy content in organic compounds depending on the nominal oxidation state of carbon (NOSC), (ii) approaches to assess EUE, (iii) similarities and differences between CUE and EUE, and (iv) discuss mechanisms responsible for lower EUE compared to CUE. The energy content per C atom (enthalpy of combustion, the total energy stored in a compound) in organic compounds is very closely (R 2 = 0.98) positively related to NOSC and increases by 108 kJ mol −1 C per one NOSC unit. For the first time we assessed the NOSC of microbial biomass in soil (−0.52) and calculated the corresponding energy content of −510 kJ mol −1 C. We linked CUE and EUE considering the NOSC of microbial biomass and element compositions of substrates utilized by microorganisms. The mean microbial EUE (0.32–0.35) is 18% lower than CUE (0.41) using glucose as a substrate. This definitely indicates that microbial growth is limited by energy relative to C. Based on the comparison of a broad range of processes of C and energy utilization for cell growth and maintenance, as well as database of experimental CUE from various compounds, we clearly explained five mechanisms and main factors why EUE is lower than CUE. The two main mechanisms behind lower EUE versus CUE are: (i) microbial recycling: C can be microbially recycled, whereas energy is always utilized only once, and (ii) chemical reduction of organic and inorganic compounds: Energy is used for reduction, which is ongoing without C utilization.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 teacher head, 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".