CAN YOU REALLY PUT A PRICE ON A GOOD NIGHT'S SLEEP
Bibliographic record
Abstract
This article focuses on investigation on to what extent would inhabitants of dwellings be willing to pay extra price for an apartment with increased sound insulation.A typical 3 room apartment in Bratislava was chosen as a case study.Online listening tests were used as a tool to understand the preferred price-quality ratio.The primary financial value of a case study apartment was estimated on real estate market in the third quarter of 2020.Later, nine (9) different variants of sound insulation of partition walls separating dwellings were considered.These walls were chosen on the basis of different construction system, two heavy-weight: (1) brick and (2) concrete, and (3) light-weight double walls-based gypsum boards.Each construction base was divided into three categories of defined by weighed sound transmission index Rw (53 dB, 55 dB and 59 dB).The four types of sounds (pink noise, cough, quarrel, party noise) were filtered through the wall spectra, e.g.sound transmission index R (dB) of chosen walls.These sounds were presented during the online listening test along with a total price for the particular apartment.The test subjects were asked to decide, which apartment they would choose based on an increased acoustic comfort and increase price of the apartment.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.005 |
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".