Windows and doors: world market outlook
Bibliographic record
Abstract
The CSIL Market Research Windows and Doors: world market outlook, contains current and historical data (production, consumption, imports, exports) and analysis of Window and Door industry for a total of 70 countries. It focuses on the 30 most important Windows and Doors markets: Argentina, Australia, Austria, Belgium, Brazil, Canada, China, Czech Republic, Denmark, Finland, France, Germany, Italy, Japan, Mexico, Netherlands, Norway, Poland, Portugal, Romania, Russia, Singapore, South Korea, Spain, Sweden, Switzerland, Taiwan, Turkey, United Kingdom and the United States. Data on international trade of windows and doors cover (in addition to the 30 countries listed above), 40 other countries, for a total of 70 countries. Part I deals with production, consumption and international trade of Windows and Doors and includes a section on world Windows and Doors statistics and an appendix with methodology notes; Part II consists of 30 country analysis tables, which include: Windows and Doors industry trends of production, apparent consumption, exports, imports for the years 2003-2012 and forecasts of yearly changes in Windows and Doors consumption in 2013 and 2014 - Major trading partners (countries of origin of imports and destination of exports of Windows and Doors)- nformation on breakdown of production by material (wood, metal, plastics) for a selection of countries; Part III includes Short profiles of major Windows and Doors manufacturers associations worldwide and a list of major sector fairs; Part IV contains addresses of top Windows and Doors manufacturers worldwide.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.019 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.059 | 0.090 |
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".