Bibliographic record
Abstract
CSIL report The world market for outdoor lighting offers a full analysis of the outdoor lighting fixtures market worldwide. This study provides outdoor lighting industry statistics (consumption data), sales data and market shares of the top manufacturers. World is considered such as the aggregate of 70 monitored countries, selected by CSIL on the basis of the size of their economy, the importance of their lighting fixtures sector and their contribution to the world trade of lighting fixtures, plus an estimation of the market value in the Rest of the World, given by the value of Chinese exports worldwide minus the value of Chinese exports in the other 69 considered countries. The GEOGRAPHICAL CLASSIFICATION of the 70 monitored countries is as follows: North America: Canada, Mexico and the United States; Central and South America: Argentina, Brazil, Chile, Colombia, Venezuela; European Union (27) + United Kingdom, Norway and Switzerland: Austria, Belgium (including Luxembourg), Bulgaria, Croatia, Cyprus, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Latvia, Lithuania, Malta, Netherlands, Norway, Poland, Portugal, Romania, Slovakia, Slovenia, Spain, Sweden, Switzerland, United Kingdom; Russia, Serbia, Turkey and CIS Countries: Belarus, Kazakhstan, Russia, Serbia, Turkey, Ukraine; China; India; Japan; Asia Pacific: Indonesia, Malaysia, Philippines, Singapore, South Korea, Taiwan, Thailand, Vietnam; Oceania: Australia, New Zealand; Middle East and Africa: Algeria, Bahrain, Egypt, Israel, Jordan, Kuwait, Lebanon, Morocco, Oman, Qatar, Saudi Arabia, South Africa, Tunisia, United Arab Emirates. For each Regional cluster, market size, activity trend and market shares are provided. Outdoor lighting is analyzed according to the following SEGMENTS: Residential/Consumer lighting (home gardens lighting and architectural lighting for common spaces in residential buildings); Urban landscape lighting (architectural lighting for city centers, city beautification); Christmas and Event lighting; Streets and major roads lighting; Lighting for tunnels and galleries; Campus/area lighting (sporting plants, open air parking, petrol stations, airports). For each market segment, market size, activity trend and market shares are provided. The Report includes PROFILES OF 85 CITIES WORLDWIDE with a selection of economic and demographic indicators and hard facts on the potential market for outdoor lighting fixtures.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.038 | 0.007 |
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".