Belowground biomass dynamics in the Carbon Budget Model of the Canadian Forest Sector: recent improvements and implications for the estimation of NPP and NEP
Bibliographic record
Abstract
In the Carbon Budget Model of the Canadian Forest Sector (CBM-CFS2), root biomass and dynamics are estimated using regression equations based on the literature. A recent analysis showed that some of these equations might overestimate belowground net primary production (NPP B ). The objectives of this study were to update the compilation of root biomass and turnover data, to recalculate the regression equations and to evaluate the impact of the new equations on CBM-CFS2 estimates of net primary production (NPP) and net ecosystem production (NEP). We updated all equations based on 635 pairs of aboveground and belowground data compiled from published studies in the cold temperate and boreal forests. The new parameter for the equation to predict total root biomass for softwood species changed only slightly, but the changes for hardwood species were statistically significant. A new equation form, which improved the accuracy and biological interpretation, was used to predict fine root biomass as a proportion of total root biomass. The annual rate of fine root turnover was currently estimated to be 0.641 of fine root biomass. A comparison of NPP estimates from CBM-CFS2 with results from field measurements, empirical calculations and modeling indicated that the new root equations predicted reasonable NPP B values. The changes to the root equations had little effect on NEP estimates.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| 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".