Repeat‐pulse <sup>13</sup> CO <sub>2</sub> labeling of canola and field pea: implications for soil organic matter studies
Bibliographic record
Abstract
Both the quantity and quality of plant residues can impact soil properties and processes. Isotopic tracers can be used to trace plant residue decomposition if the tracer is homogeneously distributed throughout the plant. Continuous labeling will homogeneously label plants but is not widely accessible because elaborate equipment is needed. In order to determine if the more accessible repeat-pulse labeling method could be used to trace plant residue decomposition, this labeling procedure was employed using (13)CO(2) to enrich field pea and canola plants in a controlled environment. Plants were exposed weekly to pulses of 33 atom% (13)CO(2) and grown to maturity. The distribution of the label throughout the plant parts (roots, stem, leaves, and pod) and biochemical fractions (ADF and ADL) was determined. The label was not homogeneously distributed throughout the plant; in particular, the pod fractions were less enriched than other fractions indicating the importance of continuing labeling well into plant maturity for pod-producing plants. The ADL fraction was also less enriched than the ADF fraction. Because of the heterogeneity of the label throughout the plant, caution should be applied when using the repeat-pulse method to trace the fate of (13)C-labeled residues in the soil. However, root contributions to below-ground C were successfully determined from the repeat-pulse labeled root material, as was (13)C enrichment of soil within the top 15 cm. Canola contributed more above- and below-ground residue C than field pea; however, canola was also higher in ADF and ADL fractions indicating a more recalcitrant residue.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".