Increasing tree productivity does not translate into greater soil organic carbon storage
Bibliographic record
Abstract
Increasing soil organic carbon (SOC) storage is one of the promising solutions to mitigate climate change. Fast-growing trees are a potential tool in this context as they rapidly accumulate C in their biomass and could transfer more organic matter (OM) into the soil. However, the relationship between aboveground productivity and SOC storage remains poorly understood. Five clones with different growth rates were selected from a 14-year-old hybrid poplar plantation located in New Liskeard, ON, Canada. We collected soil cores at 87.5 and 175.0 cm distance from the stem and at 0–20, 20–40 and 40–60 cm soil depth for soil C concentration analysis. The most productive clone DN2 (Populus deltoides × P. nigra) stored less SOC (83 Mg ha−1) between 0 and 60 cm depth than the mid-productive clones 1079 (Populus × jackii (P. balsamifera × P. deltoides)) and 915005 (P. maximowiczii × P. balsamifera) (95 and 96 Mg ha−1 respectively), while the least productive clone 747210 (P. balsamifera × P. trichocarpa) also had a lower SOC stock (85 Mg ha−1) compared to the other clones, but not significantly. There was no relationship between aboveground productivity and SOC stocks and total SOC stocks increased by 6 % when the sampling distance was closer to the tree stems. The difference in SOC stocks between clones was mostly observed at the 20–40 cm depth suggesting the significant effect of roots on SOC storage. Soil C/N ratios were significantly different between clones at 0–20 and 20–40 cm depths suggesting differences in OM decomposition rates between clones. There could be a trade-off between aboveground productivity and litter decomposition rate to increase SOC storage.
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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 | 0.001 | 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".