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Record W4415648821 · doi:10.1038/s41597-025-05976-9

A global database on land use and management change effects on soil KMnO4-oxidisable organic carbon (POXC)

2025· article· en· W4415648821 on OpenAlexaff
Cécile Bessou, Damien Beillouin, Alexis Thoumazeau, Lydie Chapuis‐Lardy, Tiphaine Chevallier, Julien Demenois, Paul N. Nelson

Bibliographic record

VenueScientific Data · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoil Carbon and Nitrogen Dynamics
Canadian institutionsSmiths Detection (Canada)
FundersAgence Nationale de la RechercheEuropean Commission
KeywordsSoil carbonArable landLand useLand use, land-use change and forestrySoil healthLand managementSoil managementSoil organic matterSoil quality

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.592
Threshold uncertainty score0.527

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.045
GPT teacher head0.253
Teacher spread0.208 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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