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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".