A global database on land use and management change effects on soil KMnO4-oxidisable organic carbon (POXC)
Bibliographic record
Abstract
Soil carbon transformation is vital for ecosystem functions like food production and climate regulation. While soil organic carbon is a key soil health indicator, its sensitivity to management changes is debated. Alternative indicators, such as permanganate-oxidisable carbon (POXC), are being explored. This database compiles 10,068 comparisons of soil POXC content from 284 peer-reviewed studies published up to 2023, covering 45 countries and 63 land use types, including arable land, grassland, agroforestry, and forests. Most studies focused on arable land (n = 7,809), examining input changes (n > 500) and tillage intensity (n > 200). The most studied land-use changes were grassland conversion to arable land (n = 324) and vice versa (n = 261). The dataset includes rich metadata on geographical context, soil types, key properties (pH, clay content), POXC protocols, and data quality scores. This resource supports scientific and policy discussions on POXC's potential as a practical indicator for improving land use and soil health management.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.014 | 0.025 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.010 |
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".