Estimating net primary production of forests in the Canadian Prairie Provinces using an inventory-based carbon budget model
Bibliographic record
Abstract
The Carbon Budget Model of the Canadian Forest Sector (CBM-CFS2) is a forest inventory-based ecosystem simulation model. It has been used previously for both retrospective and projective analyses of the carbon pools and fluxes of the Canadian forest ecosystems at the national, regional, and stand level. The objective of this study was to determine and evaluate forest net primary production (NPP) in the three Prairie Provinces in west-central Canada, as estimated by the model. The model simulated an averaged aboveground NPP (NPPA) of 172 g C·m2·year1 for the regional forests, varying from 72 to 293 g C·m2·year1, depending on ecoclimatic province, forest type, age, and site productivity. Comparisons of NPPA estimates for the boreal forest (165179 g C·m2·year1) with results from direct measurements, modeling, and empirical calculations show that the CBM-CFS2 produced reasonable estimates of NPPA. The model incorporates different types of disturbances such as wildfire, harvesting, and insects and is able to evaluate NPP changes with stand age. However, belowground NPP may be overestimated, especially for young and unproductive stands. This can be explained by the current parameter estimates for the fine-root component of belowground biomass and for fine-root turnover rates.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".