Bibliographic record
Abstract
This study analyzed and predicted the office market by composing a panel simultaneous equation using office data and macroeconomic variables of downtown area of Seoul-si, Gangnam district, and Mapo/Yeouido district from second quarter of 2003 to 4th quarter of 2014. This study set office vacancy rate, office maintenance fee, CD interest rate, and index of industrial product as the influencing variables on office rental price, and set office rental price, commercial building start results, and unemployment rate as the influencing variables on office vacancy rate. According to the analysis result, it was identified that vacancy rate and CD interest rate make statistically negative effect and maintenance fee makes positive effect on office rental price, and whereas rental price fell 0.016% when vacancy rate increased 1%, the rental price increased by 1.732% when maintenance fee increased 1% and index of industrial product appeared to have very little influence. It was verified that rental price, commercial building start results, and unemployment rate made statistically significant positive effect on office vacancy rate, vacancy rate increased 2.199% when rental price increased 1%, vacancy rate increased 2.285% when building start result increased 1%, and vacance rate increased 1.363% when unemployment rate increased 1%.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".