Fallout radionuclide-based techniques for assessing the impact of soil conservation measures on erosion control and soil quality: An overview of the main lessons learnt under an FAO/IAEA Coordinated Research Project
Bibliographic record
Abstract
This paper summarizes key findings and identifies the main lessons learnt from a 5-year (2002-2008) coordinated research project (CRP) on "Assessing the effectiveness of soil conservation measures for sustainable watershed management and crop production using fallout radionuclides" (D1.50.08), organized and funded by the International Atomic Energy Agency through the Joint FAO/IAEA Division of Nuclear Techniques in Food and Agriculture. The project brought together nineteen participants, from Australia, Austria, Brazil, Canada, Chile, China, Japan, Morocco, Pakistan, Poland, Romania, Russian Federation, Turkey, United Kingdom, United States of America and Vietnam, involved in the use of nuclear techniques and, more particularly, fallout radionuclides (FRN) to assess the relative impacts of different soil conservation measures on soil erosion and land productivity. The overall objective of the CRP was to develop improved land use and management strategies for sustainable watershed management through effective soil erosion control practices, by the use of 137Cs (half-life of 30.2 years), 210Pb ex (half-life of 22.3 years) and 7Be (half-life of 53.4 days) for measuring soil erosion over several spatial and temporal scales. The environmental conditions under which the different research teams applied the tools based on the use of fallout radionuclides varied considerably - a variety of climates, soils, topographies and land uses. Nevertheless, the achievements of the CRP, as reflected in this overview paper, demonstrate that fallout radionuclide-based techniques are powerful tools to assess soil erosion/deposition at several spatial and temporal scales in a wide range of environments, and offer potential to monitor soil quality. The success of the CRP has stimulated an interest in many IAEA Member States in the use of these methodologies to identify factors and practices that can enhance sustainable agriculture and minimize land degradation. © 2012 Elsevier Ltd.
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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 | 0.036 | 0.013 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| 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 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".