Classification of Italian Farms in the FADN Database Combining Climate and Structural Information
Bibliographic record
Abstract
Although describing the primary sector of a given country is a common institutional practice, such studies usually offer aggregated information on holding rather than supplying the information required for farm-level simulations. The present study aimed to identify the main typologies of Italian farms from the 2007 database of RICA (the Italian section of the European Union's Farm Accountancy Data Network). Using a hierarchical strategy driven by climates (5) and slopes (3), farms have been grouped by super-structure, described in terms of the presence and extent of primary activities (livestock, farmland use). The resulting picture of Italian farms is based on 35 farm types, the most common of which grow low-input orchards (e.g., olive trees). On the plains in warm climatic areas, low-input orchards and arable crops dominate; in hilly and mountainous areas, mixed farms with forage crops, meadows, ovines, and cattle prevail. In more temperate areas, the most common farm type is based on intensive and field crops (e.g., durum and bread wheat). In temperate hilly and mountain areas, mixed farms combining meadows, woods, and cattle become predominant.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".