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Record W4380077697 · doi:10.1111/gcb.16813

Mapping global soil acidification under N deposition

2023· review· en· W4380077697 on OpenAlexafffund
Chen Chen, Wenya Xiao, Han Y. H. Chen

Bibliographic record

VenueGlobal Change Biology · 2023
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicSoil Carbon and Nitrogen Dynamics
Canadian institutionsLakehead University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTerrestrial ecosystemEcosystemSoil acidificationEnvironmental scienceWetlandDeposition (geology)Environmental chemistrySoil waterBiodiversitySoil pHGrasslandGlobal changeNutrientEcologySoil scienceChemistryClimate changeBiologyStructural basin

Abstract

fetched live from OpenAlex

Soil pH is critically important in regulating soil nutrients and thus influencing the biodiversity and ecosystem functions of terrestrial ecosystems. Despite the ongoing threat of nitrogen (N) pollution especially in the fast-developing regions, it remains unclear how increasing N deposition affects soil pH across global terrestrial ecosystems. By conducting a global meta-analysis with paired observations of soil pH under N addition and control from 634 studies spanning major types of terrestrial ecosystems, we show that soil acidification increases rapidly with N addition amount and is most severe in neutral-pH soils. Grassland soil pH decreases most strongly under high N addition while wetlands are the least acidified. By extrapolating these relationships to global mapping, we reveal that atmospheric N deposition leads to a global average soil pH decline of -0.16 in the past 40 years and regions encompassing Eastern United States, Southern Brazil, Europe, and South and East Asia are the hotspots of soil acidification under N deposition. Our results highlight that anthropogenically amplified atmospheric N deposition has profoundly altered global soil pH and chemistry. They suggest that atmospheric N deposition is a major threat to global terrestrial biodiversity and ecosystem functions.

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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.996
Threshold uncertainty score0.767

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.186
GPT teacher head0.344
Teacher spread0.158 · 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 designOther design
Domainnot available
GenreReview

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

Citations116
Published2023
Admission routes2
Has abstractyes

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