Regulated Lignin Structural Units and Soil Organic Carbon Content by Cowpea Peroxidase
Bibliographic record
Abstract
Peroxidases participate in lignin biosynthesis, but there is no biochemical resolution between the structural units of lignin and soil organic carbon (SOC) contents. Black-eyed beans are staple high-protein foods for millions of at-risk populations in every continent. Its cultivation in semiarid zones could be leveraged to maximize SOC sequestration. Cowpea was treated with stoichiometric mixes of mineral nutrients. Peroxidase was electrophoretically purified from leaves, and assayed for o-dianisidine (guaiacyl units) and pyrogallol (p-hydroxyphenyl units) substrate specificities. Lignin, and SOC compositions were determined by gravimetry. Sulfate-treated cowpea produced the highest lignin (318.88 kg·ha-1) because the o-dianisidine maximum velocity (Vmax) value (0.36 µM·min-1·mg-1) was higher than that for the pyrogallol (0.08 µM·min-1·mg-1), but the SOC (64.75 kg·ha-1) was low due to the guaiacyl being higher than p-hydroxyphenyl units. Peroxidase Vmax value was low (0.12 µM·min-1·mg-1) for both substrates in the control cowpea, and accordingly lignin (268.44 kg·ha-1) and SOC (42.33 kg·ha-1) compositions were very low. The pyrogallol Vmax value (0.5 µM·min-1·mg-1) was lower than the o-dianisidine value (1.0 µM·min-1·mg-1) for KK-treated cowpea, and accordingly the lignin contents (227.4 kg·ha-1) possessed variable compositions of guaiacyl and p-hydroxyphenyl units, leading to very high SOC composition (214.56 kg·ha-1). The high SOC sequestration technology involving fertilization with stoichiometric mixes of mineral nutrients could enable limited resource farmers who cultivate cowpeas as cover crop in the Sahel to improve SOM while producing their staple crop.
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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) | 0.001 | 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".