Healthy soils as a booster to EU competitiveness
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The European Union's strategic agenda for 2024–2029 prioritizes a prosperous and competitive Europe, with soil health potentially playing a role in achieving this goal. However, the current state of European soils is of concern, with over 60 % of soils not in healthy condition, as reported by the European Union’s Soil Mission Board and the EU Soil Observatory. This results not only in environmental issues, but also economic ones, as the costs of soil degradation in the EU are estimated to be higher than €50 billion per year, underscoring the need for soil health to be placed more prominently on the political agenda. Soil-related business models, including biotechnology, remediation of contaminated sites, carbon removals and farming, regenerative agriculture, and agritech solutions, can contribute to EU competitiveness. These business models may help address most of the challenges posed by soil degradation, climate change, and biodiversity loss, while promoting sustainable agriculture practices and improving ecosystem functioning. The EU's soil remediation market is valued at €8.5 billion, with an annual growth rate of 5 %. The EU Carbon Removals and Carbon Farming Regulation provides a framework for certifying carbon removals, with potential revenue of €6 billion per year. Regenerative agriculture, which prioritises soil health and ecosystem services, can increase crop yields, reduce dependency on synthetic fertilisers and pesticides, and promote biodiversity. Agritech solutions, such as precision agriculture and artificial intelligence, can optimize farming practices, reduce costs, and improve environmental sustainability. Here we present the potential of soil-related business models to contribute to EU competitiveness, while addressing environmental and societal challenges. However, a number of challenges remain and need to be addressed as the need for acceleration, a clear policy framework, a closer collaboration of different actors in the food supply chain and a digital transformation are still needed.
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.
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) | 0.001 | 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 it