Bibliographic record
Abstract
Banking and financial collapse of late 2008 extremely heavily hit the automotive industry in most countries. In 2009, the production of cars in the world dropped to 57 million units compared to 68 million in 2007. At the same time, recent statistics show that the industry is rapidly recovering from the worst crisis in its history. In the 1st quarter of 2010 car production in the world increased by 57% compared to the same period of 2009. In China, Canada, Mexico and Great Britain it increased by more than 70%. Volkswagen, Ford Motor Company and FIAT announced major investment plans, particularly in China and Latin America. Accordingly, it is expected that in 2010 the global car production will grow to 70 million units, and to 88 million by 2016, 40% of all sales will be in the Asia-Pacific region. Reduction of the automotive industry in Russia turned out to be deeper than anywhere else – 49% in 2009 against the previous year's level. For comparison: in the United States reduction amounted to 21%, in Spain – to18, in Japan – to10, in the UK – to 6.4, in Italy – to 0.2; while in China the production grew by 44%. Nevertheless, the Russian automotive industry is also showing signs of recovery, primarily because of the governmental program of recycling old cars.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.023 | 0.006 |
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".