Quantitative analysis of demand for restoration services in Jelgava old town quarter development example
Bibliographic record
Abstract
The purpose of the article "Quantitative Analysis of Demand for Restaurant Services in Jelgava Old Town Quarter Development Example" is to find out the demand for restoration service in Jelgava Old Town Quarter and to determine the factors influencing the development of the service.The study uses a qualitative method of demand estimation through a survey and using significance scale.The survey was attended by museum professionals from the region and other stakeholders.The restoration service is much needed, but there is a lack of restorers and there is insufficient funding to spend on restoration.The survey results clearly show that there is a high demand for all types of restoration because of the large number of items to be restored.Respondents value the need to preserve the cultural heritage, the importance of scientific research, the high quality of restoration services, but less importance on the importance of education.Crafts are a way of preserving and passing on to the next generation the cultural values that have been accumulated, maintained and restored.The main factors influencing the demand for restoration are the availability of information and specialists; services provided by restoration workshops; access to finance; demand for cultural and historical heritage.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".