Belowground carbon allocation of Rocky Mountain Douglas-fir
Bibliographic record
Abstract
Carbon allocation to fine roots and mycorrhizae constitute one of the largest carbon fluxes in forest ecosystems, but these fluxes are also among the most difficult to measure. We measured belowground carbon fluxes in two Pseudotsuga menziesii (Mirb.) Franco var. glauca stands. We used a carbon balance approach to estimate total belowground carbon allocation (TBCA) and carbon allocation to fine-root and mycorrhizal production (NPPfr). The stands differed in belowground biomass because of fertilization treatment 8 years prior. Annual soil flux was 856 and 849 g C·m2·year1for the two stands. Annual root respiration equaled 269 and 333 g C·m2·year1in the low- and high-biomass stand, respectively. TBCA equaled 733 and 710 g C·m2·year1in the low- and high-biomass stand, respectively. Calculated NPPfrequaled 431 g C·m2·year1in the low-biomass stand and 334 g C·m2·year1in the high-biomass stand; equivalent to 59 and 47% of TBCA, respectively. Fine-root and mycorrhizal turnover equaled 1.8 and 0.8 year1in the low- and high-biomass stands, respectively. Belowground carbon allocation appeared to be distributed evenly between respiration and production despite differences in biomass and turnover. Sensitivity analysis indicated the NPPfrestimate is dependent foremost on the annual prediction of soil CO2flux. The carbon balance approach provided a simple nonintrusive method for separating the belowground autotrophic and heterotrophic carbon budget.
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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".