Using woody genera for phytogeographic regionalization at a medium scale: a case study of Italy
Bibliographic record
Abstract
We present a phytogeographic regionalization based on native woody flora, identifying the most useful taxonomic level, geographic variables, and orographic pattern, selecting Italy as a case study. We generated seven distance matrices among the 20 administrative regions, and using Pearson’s correlation coefficients and PCA, we verified whether distances between regions were invariant across the different sampling strategies. Once this invariance was established, we focused on genera representation. We defined two orographic indices and performed Kruskal–Wish multidimensional scaling and K-means clustering to assess Italy’s phytogeographic regionalization. A major north–south and a minor east–west gradient described the relationships between regions. Floristic diversity was strongly correlated with the region’s orography, with hills being the most important orographic feature that increased plant diversity; the effect of the orographic patterns was independent from the geographic clines observed. Despite the coarse scale, our phytogeographic regionalization comprising six clusters (variables = 133 woody genera) was consistent with previous ones based on the endemic flora (variables = 1371 units) or on bioclimatic approaches. In particular, the phytogeographic uniqueness of Northern and peninsular Italy, and of Sardinia Island, was confirmed. The next step will be to test our method at a finer scale.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".