Sustainability Nexus AID: soil health
Bibliographic record
Abstract
Abstract The Sustainability Nexus Analytics, Informatics, and Data (AID) Programme of the United Nations University (UNU), aims to provide information, data, computational, and analytical tools to support the sustainable management and long-term security of natural resources using a nexus approach. This paper introduces the Soil Health Module of the Sustainability Nexus AID Programme. Healthy soil is crucial for life on Earth, and it is essential for ecosystem services and functioning, access to clean water, socioeconomic structure, biodiversity, and food security for the growing population of the world. Healthy soils contribute to mitigating the effects of climate change and reduce the consequences of extreme events such as flooding and drought. Healthy soils influence the hydrologic cycle by regulating transpiration, water infiltration, and soil water evaporation affecting land–atmosphere interactions. The Soil Health Module of the UNU Sustainability Nexus AID Programme aims to evolve into the ultimate focal point, supporting a diverse array of stakeholders with state-of-the-art data and tools that are essential for soil health monitoring and projection. This paper discusses the importance of adopting a nexus approach for ensuring soil health, explores the AID tools currently at our disposal for quantifying and predicting soil health, and concludes with recommendations for future effort and direction within the Sustainability Nexus AID Programme concerning soil health.
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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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.003 |
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".