Soil Carbon Dynamics beneath Switchgrass as Indicated by Stable Isotope Analysis
Bibliographic record
Abstract
Abstract Surface (0–40 cm) soil organic carbon (SOC) dynamics were studied beneath four switchgrass (Panicum virgatum L.) field trials in the southeastern United States. Soil organic carbon was partitioned into particulate organic matter (POM) and mineral‐associated organic matter (MOM). Most (75–90%) of the SOC at each study site was affiliated with MOM (<0.053 mm). Changes in stable carbon isotope ratios were used to derive carbon inputs to and losses from POM and MOM at each site. Inventories of existing SOC and new C4‐derived SOC beneath switchgrass decreased with increasing soil depth. Approximately 5 yr after establishment, 19 to 31% of the existing SOC inventories beneath switchgrass had been derived from new C4‐carbon inputs. Calculated turnover times of POM and MOM ranged from 2.4 to 4.3 yr and 26 to 40 yr, respectively. The turnover time of SOC in the POM fraction increased with decreasing mean annual temperature. A simple, two‐compartment model was parameterized to predict the potential for soil carbon sequestration under switchgrass. An example calculation with the model indicated a measurable and verifiable recovery of soil carbon (≈12% increase) on degraded lands through one decade of switchgrass production. The potential to sequester carbon through switchgrass cultivation will depend on initial soil carbon inventories, prevailing climate, soil type, and site management.
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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".