Singularity of Mineralization and Multifractal Distribution of Mineral Deposits
Bibliographic record
Abstract
According to the common non-linear property of several types of non-linear hazardous processes such as earthquakes,volcanos,landslides,cloud formation,rainfall,hurricanes,flooding,and mineralization that result in anomalous amounts of energy release or mass accumulation confined to narrow intervals in space or time,these types of processes can be termed as singular processes.The end products of these non-linear processes can be modeled as fractals or multifractals.Most types of hydrothermal mineral deposits are genetically associated with mantle events and plate tectonics which themselves shows self-orginazed creticility.Here we show that not only the relationships between mineral deposits size and the number of deposits(size and number model) and between ore grade and the number of deposits(grade and number model) may follow power-law models,but also the element concentrations in a mineral district and posterior probability of an unit area containg deposits calculated by weiths of evidence method for prediction of mineral deposits may also follow power-law distribution with area.The singularity theory and non-linear models proposed have provided useful ideas and powerful tools for quantitative assessment of mineral 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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".