Non-Linear Mineralization Model and Information Processing Methods for Prediction of Unconventional Mineral Resources
Bibliographic record
Abstract
A non-linear mineralization model was proposed on the basis of a classical igneous differentiation mineralization model which can describe the generation of multi-fractal distribution of element concentration as well as grade-tonnage fractal/multi-fractal model. The work has also led to a model to explain the common properties of mineralization and mineralization-associated geochemical anomaly diversity and generalized self-similarity of the anomalies. Generalized self-similarity is related to the generalized scaling invariance which can characterize the external diversity and internal similarity of natural phenomena including mineralization and occurrence of mineral deposits. The models based on core principal of generalized self-similarity and singularity analysis have been applied to a case study of Co-Ni-Ag-As-Pb mineral resources assessment in the Gowganda area of Abitibi district, northern Ontario, Canada. The results have demonstrated that the non-linear models proposed in the current research are effective for delineating week lake sediment and water geochemical anomalies caused by deeply buried sources or week anomalies superimposed to low background values.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".