Simulation of Mediterranean forest carbon pools under expected environmental scenarios
Bibliographic record
Abstract
Simulating the effects of possible environmental changes on the forest carbon budget requires the use of calibrated and tested models of ecosystem processes. A recently proposed simulation approach based on the use of the BIOME-BGC model was applied to yield estimates of present carbon fluxes and pools in Tuscany forests (central Italy). After the validation of these estimates against existing ground data, the simulation approach was used to assess the impact of plausible climate changes (+2 °C and increased CO2 concentration) on forest carbon dynamics (gross primary production (GPP), net primary production (NPP), and relevant allocations). The results indicate that the temperature change tends to inhibit all production and allocation processes, which are instead enhanced by the CO2 concentration rise. The combination of the two factors leads to a general increase in both GPP and NPP that is higher for deciduous oaks and chestnut (+30% and 24% for GPP and +42% and 31% for NPP, respectively). Additionally, vegetation carbon is slightly increased, while total soil carbon remains almost unchanged with respect to the present conditions. These findings are analyzed with reference to the Tuscany forest situation and previous studies on the subject.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".