Mapping the accumulation of woody biomass in Mediterranean beech forests by the combination of BIOME-BGC and ancillary data
Bibliographic record
Abstract
A modelling strategy is proposed to obtain spatially explicit estimates of net carbon accumulation in Italian beech forests. This approach is based on the use of a biogeochemical model, BIOME-BGC, which is capable of simulating all main processes of ecosystems in quasi-equilibrium conditions. The model predictions are then corrected for actual forest biomass (growing stock volume) and stand age. The method is applied to predict the current annual increment (CAI) of 30 beech forest stands in Molise, Central Italy, which have been sampled during several measurement campaigns. A preliminary test is conducted to assess the model’s ability to reproduce the interannual production variations of these stands. Trials are then carried out driving the modelling strategy with both growing stock measurements collected in the field and a recently produced growing stock map. The final CAI estimates are validated through comparison with conventionally collected dendrochronological measurements. The results obtained indicate that the modelling approach is capable of reproducing interannual variations of net primary production and estimating the ground CAIs with an acceptable accuracy and when driven by the mapped growing stock. Additionally, the CAI estimates are not affected by the silvicultural system and development stage of the observed stands.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".