Species composition, richness, and diversity of weed communities of winter arable land in relation to geo-environmental factors: a gradient analysis in mainland Italy
Bibliographic record
Abstract
The interest in knowledge of the weed communities of arable land is growing worldwide. Italy is one of the countries in Europe that is the most biodiverse, geographically and environmentally heterogeneous, and rich in arable weed species. Thus, in this study, the geo-environmental factors influencing the floristic composition, the species richness, and the Shannon diversity of weed communities of Italian winter arable crops were investigated along a gradient across mainland Italy. Original data were collected in the spring of 2018 in 106 winter cereal and legume arable fields from one fixed area plot per field. Environmental and geographic data were retrieved for each plot, including latitude, longitude, elevation, soil texture, soil pH, continentality, temperature, and precipitation. The effect of crop type was also tested. Latitude was the main driver of floristic differentiation between the studied plant communities, followed by precipitation, temperature, continentality, elevation, and longitude. Soil features and crop type had no significant effects. Higher values of species richness and Shannon diversity were found in southern areas and at higher elevations. Significant explanatory variables accounted for 11.55% of the total variation in species composition of the surveyed communities. The results are discussed and compared with those of similar studies in other Eurasian countries.
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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 | 0.000 |
| 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 | 0.000 | 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".