Variazione di Superficie e Fissazione di Carbonio in Foresta nel Territorio Montano della Regione Veneto in Riferimento Allapplicazione del Protocollo di Kyoto
Bibliographic record
Abstract
"Variation of forest surface and carbon fixation in mountain areas of the Regione Veneto (Italy) and the application of the Kyoto protocol. The Parties that have signed the Kyoto Protocol must reduce global emissions of Greenhouse Gasses (GHG) during the First Commitment Period (2008 - 2012) by at least 5% with respect to 1990. This share is 6.5% for Italy. The Kyoto Protocol lays down some measures for reducing GHG emissions, which include actions in agriculture and forestry. it will thus be possible to take emissions and absorptions resulting from land use changes into account in the National Balances. Given the widespread forests in Italy, it is very important to have an assessment of the aptitude of this sector to act as a carbon sink. In this study we analysed the variation of forestland cover in a mountain area of the Veneto Region (NE Italy). The analysis was done by comparing aerial photos taken in 1991 with orthophotos reported to 2003, by photointerpretation of points with casual distribution on sample areas, according to a stratified sampling. We estimated a statistically relevant increment of about 0.095% of forest land only up to 1500 m compared to the estimated forest cover for 1990 (about 42 ha), underlining how this low increase is mainly due to forest management. The second step was to estimate the fixed carbon in the areas where forests increased. This was achieved by collecting biometrical data in the field, and then using allometric functions. The annual carbon sink was estimated as 0.69 Mg ha-1 year-1. Comparing these results with previous studies done in the pre-alpine region we estimate the annual increment of the forest area in the whole Veneto region to be about 409.94 ha and that the total carbon sink is about 282.86 Mg C year-1. A method for estimating carbon sink in afforestation/reforestation areas is proposed that could also be applied to other sites in Italy."
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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 | 0.000 | 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) | 0.001 | 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".