【日本不動産学会シンポジウム】 山岳国立公園管理の将来 （レクリエーション・登山のための利活用を探る）
Bibliographic record
Abstract
公益社団法人全日本不動産協会、公益財団法人日本賃貸住宅管理協会、 公益社団法人日本不動産鑑定士協会連合会、一般社団法人不動産協会、 一般社団法人不動産証券化協会、公益財団法人不動産流通推進センター、 一般社団法人不動産流通経営協会、一般財団法人民間都市開発推進機構 ＜プログラム＞ パネルディスカッション パネリスト：伊藤 太一(江戸川大学国立公園研究所客員教授) 花谷 泰広(登山家 First Ascent代表 甲斐駒ヶ岳七丈小屋管理者) 熊倉 基之(環境省自然環境局国立公園課長) 久末 弥生(大阪市立大学大学院都市経営研究科教授) コーディネーター：太田 昌志(千葉商科大学国際教養学部准教授) 山岳国立公園管理の将来 (レクリエーション・登山のための利活用を探る) 【日本不動産学会シンポジウム】 9 日本不動産学会誌／Vol.35No.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.004 |
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".