Promoting the use of isotopic techniques to combat soil erosion: An overview of the key role played by the <scp>SWMCN Subprogramme</scp> of the <scp>Joint FAO/IAEA Division</scp> over the last 20 years
Bibliographic record
Abstract
Abstract The International Atomic Energy Agency (IAEA), through the Joint Division with the Food and Agriculture Organization (FAO) of the United Nations, assists its Member States in applying nuclear techniques to alleviate challenges in food safety, food security and sustainable agricultural development. The Soil and Water Management & Crop Nutrition (SWMCN) Subprogramme, within the Joint FAO/IAEA Division of Nuclear Techniques in Food and Agriculture, has made significant contributions to the development of isotopic techniques for the assessment of soil degradation and the development of efficient soil and land conservation approaches. These techniques include fallout radionuclides such as 137Cs, 210Pbex, 7Be, and 239+240Pu as well as 13C stable isotope and compound‐specific stable isotope analyses. These methodologies were developed and/or refined through the work of researchers from developed and developing countries who were selected to work within the frame of IAEA's Coordinated Research Projects (CRPs). Internal research activities implemented in the Joint FAO/IAEA's SWMCN Laboratory in Seibersdorf supported the work accomplished in the CRPs. The methodologies thus developed have been subsequently disseminated to developing countries by IAEA's Technical Cooperation Programme to assist Member States to adopt climate‐smart agriculture and reduce soil degradation that poses a threat to food security and the environment. This review paper provides an overview of the activities conducted in the frame of CRPs for combating soil erosion over the last 20 years and highlights the major achievements. Examples of the success and the impact obtained in Morocco, Madagascar, and Vietnam in using these isotopic techniques are presented.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".