Distribution of agromineral resources in space and time – a global geological perspective
Bibliographic record
Abstract
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.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".