Humus composition of mineral–microbial residue from microbial utilization of lignin involving different mineral types
Bibliographic record
Abstract
This study explored the mineral contribution of lignin to humus (HS) formation through the change of HS composition in microbial–mineral residue (MMR). The liquid shake flask culture method was adopted to collect the MMR formed through the microbial utilization of lignin in the presence of goethite, bayerite, δ-MnO2, kaolinite, and montmorillonite. The carbon (C) contents of humic-like acid (HLA), fulvic-like acid (FLA), and humin-like (HLu) in MMR, represented as CHLA, CFLA, and CHLu, respectively, coupled with the ΔlogK of the HLA alkali-soluble extract and CHLA/CFLA ratio were analyzed at 10, 30, 60, and 110 d. In terms of improving HLA aggregated on minerals, the following rule was observed: goethite > bayerite > montmorillonite > kaolinite ≈δ-MnO2. Goethite was most likely to adsorb organic molecules with a high degree of polymerization. Compared with kaolinite and montmorillonite, goethite, bayerite, and δ-MnO2 were more helpful for decreasing the molecular weight and the degree of HLA condensation. Goethite, δ-MnO2, and montmorillonite presented the greatest advantages in enhancing the relative proportions of CHLA, CFLA, and CHLu, respectively, in MMR. In MMR formed in the presence of kaolinite, goethite, and bayerite, CHLA was decreased by 14.8%, 12.0%, and 5.8%, respectively, at the end of culture, whereas the CHLA associated with δ-MnO2 was increased by 12.0%. δ-MnO2 contributed the most to the conversion of CFLA to CHLA. Due to expandability and a much greater adsorption capacity, montmorillonite was most beneficial to the accumulation of CHLu.
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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 | 0.000 | 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".