A Study on Gold Price Performance Relation Among the Countries of Us, India, Canada and Australia
Bibliographic record
Abstract
The Study of different forms of Investment plays a major role in economic Investments. Among the economic Investments Gold also plays a major role in certain countries. Gold is one of the precious yellow metals in the world and one of the major investment options by many of the countries. Many of the Countries prefer various sources of Investment opportunities, but the countries like India prefer Gold as best source of investments compared to the other avenues. Like the up trends and down trends in the stock market Gold markets also have its own performance of price hike or down or standard. Moreover there is always preference for Gold as an option to invest by Indian investors and also due to the essential need for certain events. The study depends on the secondary data for sixteen years 2003 to 2018 of percentage of performance of Gold prices among the countries like America, Australia, Canada and India. The study is done on casual research basis to know is there any relation between the said countries with the performance of price rate of Gold. It is found that there is statistically significant relation between US and India also among Australia and India in Gold price fluctuations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".