Patterns and thresholds for soil pH across Europe in relation to soil health and degradation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Soil pH indicates the level of acidity or alkalinity in the soil environment, influencing various biogeochemical and physical processes. Additionally, soil pH levels are crucial in determining the bioavailability of elements such as iron, aluminium, and heavy metals which can be harmful. As such, pH is an important soil health and degradation indicator. Although there is a well-established understanding of soil pH at localized levels, the spatial and temporal variations, as well as significant thresholds at national and continental scales, are not sufficiently documented. Here we analyse the European topsoil pH data (LUCAS) in combination with other soil properties from the LUCAS survey, to identify thresholds and spatial patterns of soil pH across Europe in relation to soil health and degradation. At the European scale we found: 1) the water balance, calculated as mean annual precipitation minus potential evapotranspiration (MAP-PET), provides essential context to interpret soil pH; 2) the shift from organic carbon-rich soils to those dominated by inorganic carbon is observed at a pH of about 7.2, however, soil moisture levels may be more critical than pH for the accumulation of soil organic carbon; 3) we identified three distinct clusters within the multivariate regression tree: acidophiles (below pH 5.2), neutrophiles (pH 5.2–6.9) and alkaliphiles (above pH 6.9), while optimum microbial diversity occurred between pH 6 and 7. Earthworm abundance, as reported by the sWorm database, is more nuanced and dependent on land use; 4) risk of degradation by heavy metals cannot be captured by a single pH threshold. Finally, we identify soil pH thresholds that can aid policymakers in identifying regions that may require protection or intervention.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it