Phototrophic Biofilms Transform Soil-Dissolved Organic Matter Similarly Despite Compositional and Environmental Differences
Bibliographic record
Abstract
Dissolved organic matter (DOM) is the most reactive pool of organic carbon in soil and one of the most important components of the global carbon cycle. Phototrophic biofilms growing at the soil-water interface in periodically flooding-drying soils like paddy fields consume and produce DOM during their growth and decomposition. However, the effects of phototrophic biofilms on DOM remain poorly understood in these settings. Here, we found that phototrophic biofilms transformed DOM similarly despite differences in soil types and initial DOM compositions, with stronger effects on DOM molecular composition than soil organic carbon and nutrient contents. Specifically, growth of phototrophic biofilms, especially those genera belonging to Proteobacteria and Cyanobacteria, increased the abundance of labile DOM compounds and richness of molecular formulae, while biofilm decomposition decreased the relative abundance of labile components. After a growth and decomposition cycle, phototrophic biofilms universally drove the accumulation of persistent DOM compounds in soil. Our results revealed how phototrophic biofilms shape the richness and changes in soil DOM at the molecular level and provide a reference for using phototrophic biofilms to increase DOM bioactivity and soil fertility in agricultural settings.
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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".