Enzymatic activity and microbial biomass in soil amended with biofuel processing byproducts
Bibliographic record
Abstract
Plant essential nutrient and carbon contained in byproducts associated with biofuel production \nhave increased their value as soil organic amendments. These byproducts include wet distillers \ngrain, and thin stillage from ethanol production, and glycerol from biodiesel production. \nHowever, the potential of using these organic materials as soil amendment has not received \nenough attention yet. As a consequence, this study aimed to assess the impact of wet distillers \ngrain, thin stillage and glycerol applied at three rates equivalent to 100, 200 or 400 kg N ha-1 in \ncase of WDG and TS or 40, 400 or 4000 kg C ha-1 applied alone or combined with 300 kg N ha- \n1 as urea in case of glycerol on enzymes activity of alkaline phosphatase, protease and \ndehydrogenase, and microbial biomass C and N content. Urea and dehydrated alfalfa were also \napplied at three rates of N for comparison as conventional amendments and reference materials. \nAmended soil was incubated in controlled growth chambers for 10 days. Addition of urea, DA, \nWDG and GL+N significantly enhanced phosphatase activity especially at the low rate. Protease \nactivity was significantly enhanced by all amendments addition especially glycerol. All \namendments increased dehydrogenase activity, especially TS treatments. With the exception of \nTS, all amendments showed variable effect but significant on MBC and MBN content. The \nsignificant impact of BPB on measured microbial parameters is probably as a consequence of \ntheir effect on microbial growth and 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".