Financial risk assessment for overseas mining investment based on matter element model
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The financial risk,one of the hardest risks which the overseas mineral investment should be faced,has a significant impact.So the assessment of financial risk is very important for the preparation of overseas mineral resource development.Firstly,this paper classified overseas mining investment financial risk,analyzed the main factors of it,and determined the weight of each index by the Delphi.Secondly,classified each index and built a matter element evaluation model for overseas mining investment.Finally,seven countries include Canada,Australia,America,Russia,South Africa,India and Brazil were assessed.The results showed that the financial risk in America,Russia,Canada,Brazil and Australia belong to moderate and low level,the level of financial risk in India and South Africa are above the average.The model has practical values and conforms to reality.
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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 | 0.001 | 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 | 0.000 |
| 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 it