Abiotic and biotic-controlled nanomaterial formation pathways within the Earth’s nanomaterial cycle
Bibliographic record
Abstract
Nanomaterials have unique properties and play critical roles in the budget, cycling, and chemical processing of elements on Earth. An understanding of the cycling of nanomaterials can be greatly improved if the pathways of their formation are clearly recognized and understood. Here, we show that nanomaterial formation pathways mediated by aqueous fluids can be grouped into four major categories, abiotic and biotic processes coupled and decoupled from weathering processes. These can be subdivided in 18 subcategories relevant to the critical zone, and environments such as ocean hydrothermal vents and the upper mantle. Similarly, pathways in the gas phase such as volcanic fumaroles, wildfires and particle formation in the stratosphere and troposphere can be grouped into two major groups and five subcategories. In the most fundamental sense, both aqueous-fluid and gaseous pathways provide an understanding of the formation of all minerals which are inherently based on nanoscale precursors and reactions. The formation of nanomaterials in aqueous fluids can be explained by four different pathways: formation by biotic and abiotic processes, coupled and decoupled with weathering processes. In the Earth’s critical zone, these pathways can be classified into 18 subcategories based on the surrounding environment.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
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".