Context is everything: an investigation of Spanish River Carbonatite and its effects on soil-plant-microorganism systems
Bibliographic record
Abstract
With growing concerns about agricultural sustainability and food security, the use of rock fertilizers and agrominerals is receiving renewed interest. A wide variety of geological resources have been proposed as crop nutrient sources, with silicate rocks the predominant focus. Carbonatite rocks are known to weather more readily than silicate rocks; yet, they have received relatively little attention as it is thought their high Ca and Mg contents hinder effective nutrient release. However, there is strong evidence that the nutrients within carbonatite rocks are easily accessible to plants, and that these rocks have noticable effects on crop plant growth. Here I propose a framework to understand the mode of action of carbonatites on soil fertility and plant nutrition by integrating research at multiple scales, i.e., from individual plants to the ecosystem, including soil microorganisms. The model stems from greenhouse experiments on two crops, pea and wheat, and an extensive survey of the carbonatite deposit. It is emphasized that a systems-approach must be taken when examining carbonatites as their effects are strongly context-dependent, and there is evidence that a three-way interaction between plant-carbonatite-microorganisms is responsible for some of the observed effects on plants. The framework presented is intended not only to synthesize the current knowledge on carbonatites as rock fertilizers but also to guide future research on this and other similar geological resources.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".