{"id":"W2994225570","doi":"10.2495/sdp-v15-n1-1-13","title":"on the determinants of a successful, sustainable-driven adaptive reuse: A multiple regression Approach","year":2020,"lang":"en","type":"article","venue":"International Journal of Sustainable Development and Planning","topic":"Cultural Heritage Management and Preservation","field":"Arts and Humanities","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Adaptive reuse; Reuse; Regression; Regression analysis; Sustainability; Sustainable development; Econometrics; Multivariate adaptive regression splines; Computer science; Environmental science; Statistics; Engineering; Mathematics; Machine learning; Polynomial regression; Civil engineering; Ecology; Waste management; Biology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01021475,0.001359567,0.001400945,0.004592717,0.001004608,0.003180965,0.001969625,0.001268334,0.01103799],"category_scores_gemma":[0.02598907,0.0005366589,0.002755203,0.004206173,0.001372742,0.00216122,0.002798334,0.002569458,0.001152816],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002304066,"about_ca_system_score_gemma":0.003741903,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03058978,"about_ca_topic_score_gemma":0.01807875,"domain_scores_codex":[0.9905433,0.006649596,0.0002821195,0.0008892404,0.0007810359,0.0008547498],"domain_scores_gemma":[0.9546753,0.03925201,0.002775501,0.0009102066,0.001781608,0.0006053109],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005463051,0.001715084,0.6238443,0.001040292,0.003666356,0.002264095,0.004255725,0.1213634,0.001196324,0.1016281,0.003920497,0.1345596],"study_design_scores_gemma":[0.0000775113,0.001456689,0.2925242,0.001127389,0.002101087,0.0006364585,0.01160337,0.6284527,0.001702462,0.04728036,0.01279269,0.0002451336],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.836646,0.002791721,0.1319182,0.003864667,0.0001734303,0.0006056673,0.0009838345,0.0002694837,0.02274698],"genre_scores_gemma":[0.9818406,0.0008550899,0.01302377,0.00009052403,0.00004848596,0.0002561625,0.000233285,0.00003976174,0.003612414],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03058978,"threshold_uncertainty_score":0.0608235,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08908956843697252,"score_gpt":0.2604909943772815,"score_spread":0.171401425940309,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}