An Analysis of Global Iron Ore Resource Market Trend in the Post-COVID-19 Period
Bibliographic record
Abstract
Iron is the most widely used metal in China. There have been many changes in the international iron ore market due to the covid-19. Analyzing the reasons for the changes in the international iron ore supply, demand and market structure and predicting the future trends are of great significance for the stable supply of iron ore. This paper first analyzes the global steel production, iron ore supply and price trends under the covid-19 and believes that the global iron ore supply and demand pattern is further concentrated, showing a pattern of countries, two 60%. That is to say, China's steel production will further improve its global share, approaching 60%;Australia's supply share in the global iron ore shipping market will further increase, approaching 60%, because of the covid-19. Secondly, this paper predicts the changing trend of China's and global steel demand in the next 2-3 years, and believes that the main reason for the increase in China's steel production in recent years is the country's need for stable economic growth. China's steel production will remain high in the next 2~3 years, but in the long run, China's iron ore demand will slow down after a period of time. Finally, this paper analyzes the global iron ore price trend and believes that the global iron ore price will rise and fall to less than 100 US dollars/ton in the fourth quarter of 2020. The iron ore price will slowly fluctuate and fall down to 60~80 US dollars/ton in the next 2~3 years. © 2021, Science Press. All right reserved.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".