A novel strategy for synthetic microbial community conversion of liquid pig manure into microbial fertilizer
Bibliographic record
Abstract
The pig industry generates copious amounts of liquid pig manure (LPM), which poses a great challenge to the environment; conventional treatment of the manure is often time-consuming and inefficient. We developed a novel technique for converting LPM into liquid microbial fertilizer with two steps: (1) screened ammonia nitrogen-degrading strains and synthetic microbial communities (SynCom) were used in combination with H 2 O 2 for the biological deodorization of LPM; and (2) the bio-deodorized and autoclaved LPM was then inoculated with screened SynCom2 to produce liquid microbial fertilizers. The results of bio-deodorization demonstrated that both single ammonia nitrogen-degrading strains and SynCom1 reduced over 90% of the odor and GHGs emissions (NH 3 , H 2 S, CO 2 and CH 4 ) from LPM, and decreased the pH, chemical oxygen demand (COD), ammoniacal nitrogen (NH 4 + -N) and EC to varying extents. The SynCom1 was able to completely eliminate malodorous volatile organic compounds (p-methyl phenol) from LPM, outperforming single ammonia nitrogen-degrading strains. The deodorized LPM treated with SynCom2 exhibited high levels of nutrient concentration (31.58 g/L of total carbon and 3.81 g/L of total nitrogen), microbial biomass (up to 1.92 × 10 10 CFU/mL), plant safety and maturity (germination index > 100%), indicating suitability as microbial fertilizer. Cultivation experiments demonstrated a significant increase of over 70% in shoot length, root length, fresh weight and dry weight of rice seedlings when microbial fertilizers were applied in black soil. Overall, our study provided a valuable direction for the development of potential industrial applications.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".