A Study on the Heritage Density Transfer system in the city of Vancouver, BC, Canada.
Bibliographic record
Abstract
本論文の目的は、都心における歴史的建築物保全の観点からバンクーバー市の容積移転制度の運用実態を明らかにすることである。日本の都心部において多くの歴史的建築物が取り壊され、地区の特性に合わない開発が行われている。日本では用途地域の緩さや多くの容積ボーナス制度の存在等の課題があり、効果的な容積移転制度のあり方が問われている。そこで本研究では、バンクーバー市で公開されている容積移転可能物件を対象として、容積移転制度運用実態の分析を行った。その結果、容積移転制度を効果的に運用するための要素として、厳しいゾーニング、裁量的な都市計画システム、容積緩和を可能とするリゾーニング制度が挙げられ、容積の経済価値換算に関してはバンクーバー市においても試行錯誤の段階であることが分かった。
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".