Application of some multidimensional comparative analysis methods to investigate secondary real estate market in Warsaw
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The paper aims at application of multidimensional comparative analysis methods to investigating Warsaw real estate secondary market. The research is based on a data base covering more than 300 apartments offered in the fourth quarter of 2010. There were considered basic characteristics such as the price per 1 square meter, apartment area, number of rooms, floor the apartment is located on, number of storeys in a building and the year of building completion. Application of two alternative methods: Ward’s method and k-means method allowed to divide the set of apartments into 5 clusters. The results obtained may be interesting for both individual investors and real estate agents as they help to filter the set of apartments and to find offers similar to the apartment with a prespecified characteristics.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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 it