Forest carbon stocks in Newfoundland boreal forests of harvest and natural disturbance origin II: model evaluation
Bibliographic record
Abstract
The Intergovernmental Panel on Climate Change recommends that countries that use advanced (Tier 3) models to meet their international reporting obligations on forest greenhouse gas emissions and removals evaluate model predictions against independent field data. Unfortunately, estimates of total ecosystem C stocks and stock changes are scarce and consequently the recommended evaluations are rarely completed. The Carbon Budget Model of the Canadian Forest Sector (CBM-CFS3) is the core model of Canada’s National Forest Carbon Monitoring, Accounting, and Reporting System that implements an Intergovernmental Panel on Climate Change Tier 3 approach. It accounts for biomass, dead organic matter, and soil C pools as affected by natural and anthropogenic disturbances. We used data from a recent study of total ecosystem C stocks for black spruce ( Picea mariana (Mill.) BSP) and balsam fir ( Abies balsamea (L.) Mill.) boreal forest chronosequences of different disturbance origins in Newfoundland, Canada, to evaluate C stock and stock change predictions from the CBM-CFS3. Results indicated that the accuracy of the CBM-CFS3 is high for landscape-scale estimation of C stocks. Comparison of estimates stratified by lead species or disturbance type indicated that model accuracy could be improved at finer scales by increasing specific model parameters such as the snag fall rate and woody debris decay rates relative to default parameters.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".