{"id":"W2625605279","doi":"10.3390/buildings7020051","title":"Measuring and Interpreting Urban Externalities in Real-Estate Data: A Spatio-Temporal Difference-in-Differences (STDID) Estimator","year":2017,"lang":"en","type":"article","venue":"Buildings","topic":"Housing Market and Economics","field":"Economics, Econometrics and Finance","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"","keywords":"Estimator; Econometrics; Spatial econometrics; Panel data; Real estate; Externality; Spillover effect; Spatial analysis; Computer science; Economics; Statistics; Mathematics; Microeconomics; Finance","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001018579,0.0001945969,0.0005015167,0.0002496982,0.0002047775,0.0006623307,0.0007159794,0.00008617131,0.00003272356],"category_scores_gemma":[0.0004009677,0.0002288242,0.00003339649,0.00004493311,0.0001337716,0.0007393626,0.0004881444,0.0001890275,0.00001546081],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001051634,"about_ca_system_score_gemma":0.00002363954,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01727622,"about_ca_topic_score_gemma":0.004346132,"domain_scores_codex":[0.9983496,0.00001691484,0.0006400254,0.0005909164,0.00003306009,0.0003694865],"domain_scores_gemma":[0.99866,0.00008645836,0.0005031181,0.0006612219,0.00001038109,0.0000787645],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003080869,0.00002047997,0.9844717,0.00004803262,0.000008576178,0.00001068202,0.001639069,0.000004064529,0.000006501487,0.004442963,0.00004158816,0.009275572],"study_design_scores_gemma":[0.0006349217,0.00002698893,0.9587021,0.0003015067,0.000003525619,0.000004734075,0.0003377665,0.02673707,0.00001774564,0.01112722,0.0017141,0.0003922877],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9670175,0.000120908,0.0001762902,0.0002307231,0.0003020891,0.0001205064,0.00004449378,0.00002966325,0.03195779],"genre_scores_gemma":[0.9966704,0.001394262,0.001621982,0.00002159662,0.00007749224,0.00001246523,0.0000113522,0.0000257888,0.0001647196],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03179307,"threshold_uncertainty_score":0.9892678,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1029122442211822,"score_gpt":0.267832405586099,"score_spread":0.1649201613649168,"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."}}