Responsible plant nutrition: A new paradigm to support food system transformation
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 coming 10–20 years will be most critical for making the transition to a global food system in which mineral nutrients in agriculture must be managed in a more holistic manner. Fertilizers play a particular role in that because they are among the key drivers for securing global food security and improving human nutrition through increased crop yields and nutritional quality. A new paradigm for responsible plant nutrition follows a food systems and circular economy approach to achieve multiple socioeconomic, environmental and health objectives. Achieving that requires utilizing all available organic and inorganic nutrient sources with high efficiency, tailored to the specific features of food systems and agroecosystems in different world regions. Critical actions include: (i) sustainability-driven nutrient roadmaps, (ii) digital crop nutrition solutions, (iii) nutritious crops, (iv) nutrient recovery and recycling, (v) climate-smart fertilizers, and (vi) accelerated innovation. The outcome of this transformation will be a new societal plant nutrition optimum rather than a purely economic optimum. New partnerships and sustainability-focused business models will create added value for all actors in the nutrient chain and benefit farmers as well as consumers. Research needs to become more problem-driven and merge excellent science with entrepreneurial innovation approaches in order to develop robust solutions faster and at larger scale. Evidence-based policies should focus on creating and supporting the necessary nutrient stewardship roadmaps, including realistic national targets, progressive regulation and incentives that support technology and business innovation.
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 | 0.001 |
| 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 it