The threat-work: a network of potential threats to soil biodiversity
Bibliographic record
Abstract
Soils are estimated to contain more than half of the biodiversity on our planet, encompassing a rich spectrum of genes, organisms and functions that play a crucial role in many ecological processes, such as nutrient cycling, organic matter decomposition, and the creation of a well-structured soil matrix. However, soils encounter many threats that significantly challenge their functionality and biodiversity. The FAO Report on the State of Knowledge of Soil Biodiversity identified 12 primary threats to soil and soil biodiversity, highlighting regional and unique ecozonal perspectives. Most threats to soil come from anthropogenic land use activities and management practices associated with intensive agriculture, livestock, forestry, and other resource extraction activities, as well as industrial activities, infrastructure and urbanization, which vary in strength across various regions and ecozones. However, these threats are highly interconnected and often culminate in losses to soil organic matter (SOM) and soil organic carbon (SOC) –– also considered a threat itself –– that drives changes in physical, chemical and biological attributes of the soil environment that lead to soil biodiversity loss. We conceptualize these interlinked threats as a threat network or ‘threat-work’, where the loss of SOM plays a pivotal role. Addressing this threat-work requires a mechanistic understanding of how soil biodiversity loss occurs across diverse landscapes and ecozones. SOM is essential for creating a favorable environment for soil biodiversity by enhancing nutrient availability, water retention, and soil structure. Losses in SOM, closely tied to the mechanisms of soil biodiversity loss, alter physical, chemical, and biological soil attributes, leading to biodiversity decline. Such knowledge can identify priority areas for restoration and inform best practices to conserve soil biodiversity. Protecting and enhancing SOM is central to these efforts. By disentangling the drivers of soil biodiversity loss and their interactions within this threat network, we can develop holistic strategies to mitigate soil biodiversity loss, safeguard soil health, and ensure the sustainability of soil ecosystems globally.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".