Total belowground carbon and nitrogen partitioning of mature black spruce displaying genetic × soil moisture interaction in growth
Bibliographic record
Abstract
Total belowground biomass, soil C, and N mass were measured in plots of 32-year-old black spruce ( Picea mariana (Mill.) Britton, Sterns & Poggenb.) from four full-sib families studied previously for drought tolerance and differential productivity on a dry and a wet site. Stump root biomass was greater on the wet than on the dry site; however, combined fine and coarse root biomass was greater on the dry than on the wet site, resulting in no site root biomass differences. There were no site differences in root distribution by soil depth. Drought-tolerant families had greater stump root biomass and allocated relatively less to combined coarse and fine roots than drought-intolerant families. Fine roots (<2 mm) made up 10.9% and 50.2% of the belowground C and N biomass. Through 50 cm soil depth, mean total belowground C mass was 187.2 Mg·ha–1, of which 8.9%, 3.4%, 0.7%, and 87.0% were from the stump root, combined fine and coarse roots, necromass, and soil, respectively. Here, we show that belowground C sequestration generally mirrors (mostly from stump roots) aboveground growth, and thus, trends in genetic and genetic × environment productivity effects result in similar effects on belowground C sequestration. Thus, tree improvement may well be an important avenue to help stem increases in atmospheric CO2.
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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) | 0.000 | 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".