Comparazione delle definizioni di bosco efficaci sul territorio nazionale attraverso l’analisi di dominanza
Bibliographic record
Abstract
Abstract: Comparison of forest definitions at the national level using dominance analysis. Despite Italian forests are subject of protection since long time, the first forest definition legally binding has been enacted only in 2001. However, thanks to the action of international, national and regional institutions, 25 forest definitions are effective today on the Italian territory. All definitions of forest are fully described and their qualification analyzed using four definition types: legislation, policies, technical and allocative. Only those that are included in the first three types were subjected to dominance analysis. Macro-criteria, criteria and sub-criteria have been defined to build an absolute valuation matrix. Concordance, discordance and dominance analysis have been cattied out after normaliztion. Forest definitions have been subdivided into two groups: dominant and dominated. The first group includes all regional definitions due to the details of their content, while the second group includes definition adopted by international and national institutions. Three aspects have been emphasized in the conclusion: (a) all definitions, except that adopted by the Lombardy Region, are based on the “forest land” and not on the “forest”; (b) most definitions do not include the multifunctional nature of the forest thus neglecting one of the most relevant forest profiles; (c) the first part of all definition is devoted to technical details as if the main motivation for their elaboration were the quantification of the forest area.
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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) | 0.001 | 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) | 0.003 | 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".