D5.3. Relationship between soil biodiversity, crop yield and quality and delivey of aecosystem services
Bibliographic record
Abstract
Prokaryotes, including bacteria and archaea, play a fundamental role in agricultural soils by drivingcritical ecosystem processes such as nutrient cycling, organic matter decomposition, and soilstructure maintenance (Delgado-Baquerizo et al., 2020). These microorganisms are essential for soilfertility and plant health, as they enhance nutrient availability and promote plant growth throughvarious symbiotic interactions (Babin et al., 2019; Berendsen et al., 2012; Wagg et al., 2014).Consequently, preserving a high diversity of prokaryotes in agricultural soils is crucial. Agricultural practices significantly impact the diversity and functionality of soil prokaryotes (Babin etal., 2019; Levine et al., 2011; Tilman et al., 2002). Interventions such as tillage, crop rotation, and theapplication of fertilizers and pesticides can modify the physical and chemical properties of the soil,thereby influencing microbial communities (Cozim-Melges et al., 2024). This section of the report explores the relationships between soil prokaryotic biodiversity, crop yield,and quality, and ecosystem service delivery across the WP5 case studies conducted across sixEuropean regions. Ecosystem services analyzed include greenhouse gas emissions, soil carboncontent, nutrient levels, bulk density, pH, and other key soil chemical and physical parameters.Findings are based on results from the final monitoring period for each case study. Data arepresented separately for each case study to provide detailed insights. This work was funded by the European Commission Horizon 2020 project SoildiverAgro [grant agreement 817819].
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".