Mineralization of Carbon‐14‐Labeled Plant Residues in Conventional Tillage and No‐Till Systems
Bibliographic record
Abstract
Tillage systems are known to influence the quantity and quality of soil organic C (SOC); however, it is unclear if tillage also changes the soil catabolic capacity (the ability to decompose C substrates). This study compared soils from two contrasting tillage systems, no‐till (NT) vs. conventional tillage (CT), with respect to their ability to catabolize a common 14 C‐labeled substrate. Intact core samples from the 0‐ to 5‐cm soil depth under CT and the 0‐ to 5‐ and 10‐ to 15‐cm depths under NT were collected after 14 yr of winter wheat ( Triticum aestivum L.)–corn ( Zea mays L.)–soybean [ Glycine max (L.) Merr.] rotation. Soil cores received a common substrate ( 14 C‐labeled wheat residues) either surface applied or incorporated into the soil. Decomposition of the 14 C‐labeled residues, soil mineral N concentration, and soil microbial biomass 14 C (MB 14 C) were monitored for 86 d at 25°C. Respiration of 12 CO 2 from native SOC was greater at 0 to 5 cm under NT than CT but did not vary with depth under NT; there was no significant effect of tillage system or depth under NT or residue placement on respiration of residue 14 CO 2 . Incorporation of 14 C residues increased MB 14 C compared with surface application, indicating a greater efficiency of C utilization. For the soils and residues used in this experiment, there was no evidence of an effect of tillage system on soil catabolic capacity, and differences in soil microbial activity between tillage systems were attributed only to differences in the quantity and quality of C substrates.
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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 | 0.001 |
| 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".