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Record W2194582938 · doi:10.5194/soil-2-79-2016

World's soils are under threat

2016· article· en· W2194582938 on OpenAlexaff
Luca Montanarella, D.J. Pennock, N. J. McKenzie, Mohamed Badraoui, V. O. Chude, Isaurinda Baptista, Tekalign Mamo, Martin Yemefack, Mikha Singh Aulakh, K. Yagi, Suk Young Hong, Pisoot Vijarnsorn, Gan-Lin Zhang, Dominique Arrouays, H. I. J. Black, Pavel Krasilnikov, Jaroslava Sobocká, Julio Alegre, Carlos Roberto Henriquez, Maria de Lourdes Mendonça-Santos, Miguel Ángel Taboada, David Espinosa‐Victoria, Abdullah Alshankiti, Seyed Kazem Alavipanah, Elsiddig A. E. Elsheikh, Jon Hempel, Marta Camps Arbestain, Freddy Nachtergaele

Bibliographic record

VenueSOIL · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoil Carbon and Nitrogen Dynamics
Canadian institutionsUniversity of Saskatchewan
FundersNatural Environment Research CouncilEuropean CommissionSight Research UK
KeywordsEnvironmental scienceSoil waterSoil carbonSoil governanceFertilizerEnvironmental protectionSoil organic matterNatural resource economicsSoil scienceAgronomySoil healthBiology

Abstract

fetched live from OpenAlex

Abstract. The Intergovernmental Technical Panel on Soils has completed the first State of the World's Soil Resources Report. Globally soil erosion was identified as the gravest threat, leading to deteriorating water quality in developed regions and to lowering of crop yields in many developing regions. We need to increase nitrogen and phosphorus fertilizer use in infertile tropical and semi-tropical soils – the regions where the most food insecurity among us are found – while reducing global use of these products overall. Stores of soil organic carbon are critical in the global carbon balance, and national governments must set specific targets to stabilize or ideally increase soil organic carbon stores. Finally the quality of soil information available for policy formulation must be improved – the regional assessments in the State of the World's Soil Resources Report frequently base their evaluations on studies from the 1990s based on observations made in the 1980s or earlier.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.026
Threshold uncertainty score0.088

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0050.003
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0260.007

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 head0.021
GPT teacher head0.215
Teacher spread0.193 · 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 source (direct Gemma or distilled Codex), 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

Citations459
Published2016
Admission routes1
Has abstractyes

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