A dataset for soil organic carbon in agricultural systems for the Southeast Asia region
Bibliographic record
Abstract
Authors: Federico Gomez1*, Ana Carcedo1, Chan Makara Mean2, Manuel Reyes3, Lyda Hok4, Florent Tivet5, Vang Seng6, P. V. Vara Prasad1, Shopie Manson7, K. A. I. Nekaris7, Eva Lehndorff8, Thilde Bech Bruun9, Catherine M. Hepp10, Ryusuke Hatano11, Auldry Chaddy12, Pham Thi Thu Huong13, Selva Dhandapani14, Katharina Maria Keiblinger15, Rizki Maftukhah16, Zar Ni Zaw17, Sota Tanaka18, and Ignacio Ciampitti1* 1. Department of Agronomy, Kansas State University, Manhattan, Kansas, US. 2. Faculty of Agricultural Biosystems Engineering, Royal University of Agriculture, Phnom Penh, Cambodia.3. Sustainable Intensification Innovation Lab, and Department of Agronomy, Kansas State University, Manhattan, Kansas, US. 4. Center of Excellence on Sustainable Agricultural Intensification and Nutrition, Royal University of Agriculture, Phnom Penh, Cambodia.5. CIRAD, UPR AIDA, Univ Montpellier, Montpellier, France. Agroecology for South-East Asia. 6. Department of Agricultural Land Resources Management, Phnom Penh, Cambodia. 7. School of Humanities and Social Sciences, Oxford Brookes University, Oxford, United Kingdom. 8. Soil Ecology, Universität Bayreuth, Bayreuth, Germany. 9. Department of Geosciences and Natural Resource Management, Section for Geography, University of Copenhagen, Copenhagen, Denmark. 10. Lethbridge, AB, Canada. 11. Research Faculty of Agriculture, Hokkaido University, Hokkaido, Japan. 12. Sarawak Tropical Peat Research Institute, Sarawak, Malaysia. 13. Horticulture Department, Field Crops Research Institute, Hai Duong, Vietnam. 14. Department of Geography and Environmental Science, School of Archaeology, Geography and Environmental Sciences (SAGES), University of Reading, Reading, United Kingdom. 15. Department of Forest and Soil Sciences, Institute of Soil Research, University of Natural Resources and Life Sciences, Vienna, Austria. 16. Department of Agricultural and Biosystem Engineering, Faculty of Agricultural Technology, Universitas Gadjah Mada, Indonesia. 17. Faculty of Natural Resources, Prince of Songkla University, Thailand. 18. Faculty of Agriculture and Marine Sciences, Kochi University, Kochi, Japan. For more information about the dataset or the scripts, please contact the corresponding authors at: fmgomez@ksu.edu, ciampitti@ksu.edu.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.011 |
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".