The Role of Soil Diversity (Pedodiversity) in the Kunming-Montreal Global Biodiversity Framework: Example of the Contiguous United States of America (USA)
Bibliographic record
Abstract
The Kunming-Montreal Global Biodiversity Framework (GBF) is an important agreement committing 196 countries (the United States is not part of GBF) to reduce and stop the loss of biodiversity by 2030. Biodiversity and soil diversity (pedodiversity) are intricately linked by sharing biosphere. Similarly to biodiversity, pedodiversity is classified using various classification systems adopted by countries in the world (e.g., United States Soil Taxonomy). The loss of pedodiversity is often caused by land use and land cover (LULC) changes, which impact biodiversity. These losses need to be acknowledged and accounted for by the GBF. The innovation of this study is that it proposes to include pedodiversity and its metrics into the GBF using the contiguous United States of America (USA) and GBF targets as an example. This study proposes to use geospatial technologies (e.g., land cover change matrix) linked to soil databases to monitor temporal changes and no net loss in pedodiversity. Loss of pedodiversity can result in damages (e.g., pollution), which can harm biodiversity and ecosystem functions and services (ES). As of 2021, over two million square kilometers were anthropogenically degraded in the contiguous USA, with all ten soil orders being affected by this degradation (relevant to target ten focused on the sustainable use of natural resources). Analysis of changes in LULC between 2001 and 2021 showed an increase in anthropogenic land degradation (LD) (+3.4%), which resulted in a net loss of pedodiversity and affected all of the ten soil orders in the contiguous USA. Future GBF refinements could use pedodiversity metrics to analyze the ability to support biodiversity.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".