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Distribution of agromineral resources in space and time – a global geological perspective

2022· article· en· W4225830766 on OpenAlexaff
Peter van Straaten

Bibliographic record

VenuePesquisa Agropecuária Brasileira · 2022
Typearticle
Languageen
FieldMaterials Science
TopicClay minerals and soil interactions
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsGeologyGeochemistrySedimentary rockIgneous rockEarth scienceMetamorphic rockGlauconiteSchist

Abstract

fetched live from OpenAlex

Abstract Agromineral resources are minerals and rocks used to improve soil productivity and health. These resources can be applied: indirectly, by extracting and concentrating one or more minerals by industrial processes for the production of conventional, highly soluble fertilizers; and directly, without processing, except fine grinding, for direct soil application. Agromineral resources include sedimentary phosphates, limestones/dolostones, potash and glauconite-bearing rocks of sedimentary origin, basaltic rocks, phonolites, kamafugites, and glass-rich mafic rocks of igneous origin. Among metamorphic agromineral resources, marble and biotite schist stand out. However, agromineral resources are not equally distributed on Earth’s surface, occurring more in one area than in another, and have accumulated in various geotectonic settings related to plate tectonics, being formed in specific geological time periods in Earth’s history. Therefore, these resources occur in specific “agromineral provinces” and were formed during specific “agromineral epochs”. This paper provides a conceptual framework for agromineral resource distribution in time and space. Agrominerals have a high potential to be used as directly applied soil amendments and soil remineralizers for new and innovative farming strategies, provided the amendments are low or free of contaminants.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.862
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.013
GPT teacher head0.263
Teacher spread0.250 · 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.

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

Citations9
Published2022
Admission routes1
Has abstractyes

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