Developing a Multi-scaled Global Soil Resources Information System
Bibliographic record
Abstract
In recent years the capability for conducting global to regional scale natural resource assessments has substantially increased because multi-scaled soil resource inventories for continental, regional, national and sub-national areas are now publically-available in a digital format. This paper discusses the development of a prototype multi-scaled global soil resources information system using published geospatial information from international and national systems. The information system initially displays global soil information at various scales - 1:5,000,000 [Harmonized Word Soil Database-2008; Digitized Soil Map of the World-2003; Global Soil Orders and Suborders-2005; Land Resources of Russia-2002; Soil Map of Brazil-1981]; 1:1,000,000 [Eurasian Soil Database-2001], with selected national soil information at 1:250,000[STATSGO2-2009;Principal soil types/associations of Ghana-1999];and additional examples with the map scale ranging from 1:100,000 to 1:24,000[SSURGO-2009]. In a parallel effort, sets of global soil classification correlation tables were developed to cross-reference soil taxonomic classes from USDA Soil Taxonomy-1998; World Reference Base for Soil Resources-2007; Soil Map of the World, FAO-1990, FAO-1974; Canada, Mexico, Soil Map of Russia, Soil Map of China, Chana, Brazil and other countries. The objectives of our paper are(1) perform an inventory and collection of publicly-available digital soil resource inventories, (2) assemble multi-scaled soil resource information from national to regional geospatial domains in a GIS-based data visulaization tool, (3) utilize existing soil attribute information to develop soil use and interpretation thematic layers, (4) provide examples of geospatial output(maps, tables, graphs) from selected areas based on multi-scaled soil resource information, and (5) disseminate soil geographic information to enhance international soil science communication. Examples of regional, national and sub-national soil interpretations illustrate data visualization and analysis for selected soil interpretations. Data visualization using an Internet-based method will support increased use as an education and outreach tool that encourages scientific collaboration and helps to facilitate understanding and acceptance by non-technical users.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.017 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".