Application of the CBM-CFS3 model to estimate Italy's forest carbon budget, 1995–2020
Bibliographic record
Abstract
The estimation of past and future forest carbon (C) dynamics in European countries is a challenging task due to complex and varying silvicultural systems, including uneven-aged forest management, and incomplete inventory data time series. In this study, we tested the use of the Carbon Budget Model of the Canadian Forest Sector (CBM-CFS3) in Italy, a country exemplifying most of these challenges. Our objective was to develop estimates of forest carbon budgets of the Forest Management area (including all forests existing in 1990) for the period 1995–2009, and to simulate alternative scenarios of natural disturbance (fire) and harvest rates to 2020. A number of methodological challenges required modifications to the default model implementation. Based on National Forest Inventory (NFI) data, we (i) developed a historic library of yield curves derived from standing volume and age data, reflecting the effect of past silvicultural activities and natural disturbances, and a current library of yield curves derived from the current net annual increment; (ii) reconstructed the age structure for a period antecedent to the reference NFI year (2005), to compare the model results with data from other sources; and (iii) developed a novel approach for the simulation of uneven-aged forests. For the period 2000–2009, the model estimated an average annual sink of −23.7 Mt CO2 yr−1 excluding fires in Italy's managed forests. Adding fires to the simulation reduced the sink to −20.5 Mt CO2 yr−1. The projected sink (excluding all fires) for the year 2020 was −23.4 Mt CO2 yr−1 assuming average (2000–2009) harvest rates. A 36% increase in harvest rates by 2020 reduced the sink to −17.3 Mt CO2 yr−1. By comparing the model results with NFI data and other independent studies, we demonstrate the utility of the CBM-CFS3 both for estimating the current forest sink in even-aged and more complex uneven-aged silvicultural systems in Italy, and for exploring the impact of different harvest and natural disturbances scenarios in managed forests. This study demonstrates the utility of the CBM-CFS3 to national-scale estimation of past and future greenhouse gas emissions and provides the foundation for the model's future implementation to other European countries.
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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 | 0.001 | 0.001 |
| 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".