{"id":"W4295631026","doi":"10.1080/01621459.2022.2097086","title":"Comments on “Measuring Housing Vitality from Multi-Source Big Data and Machine Learning”","year":2022,"lang":"en","type":"article","venue":"Journal of the American Statistical Association","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta; Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Vitality; Big data; Computer science; Psychology; Data science; Statistics; Econometrics; Mathematics; Data mining; Philosophy","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01810931,0.001469091,0.001344306,0.001577692,0.006158713,0.0058231,0.003282205,0.03894417,0.009744355],"category_scores_gemma":[0.1193871,0.001127188,0.002285181,0.002019393,0.004079483,0.004701542,0.004469755,0.03400037,0.008913148],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003629082,"about_ca_system_score_gemma":0.009542119,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03220882,"about_ca_topic_score_gemma":0.03544708,"domain_scores_codex":[0.9863777,0.002864619,0.001650046,0.002115139,0.00545587,0.001536549],"domain_scores_gemma":[0.9073969,0.04562183,0.005048793,0.002185938,0.03419391,0.005552661],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002625237,0.00001004189,0.0005373099,0.00005025534,0.00001301761,0.0001161153,0.0001759423,0.00007326419,0.0002038661,0.001055411,0.994871,0.00286748],"study_design_scores_gemma":[0.00005547456,0.00005183457,0.005479418,0.0005557933,0.00005255322,0.0002261294,0.001375522,0.0005975708,0.0006617103,0.003238481,0.9875708,0.000134747],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.0006757549,0.0005384252,0.0008717636,0.9312044,0.06411506,0.0000371805,0.0004049892,0.000113931,0.002038575],"genre_scores_gemma":[0.002202134,0.0003239614,0.0007415518,0.9640434,0.02719761,0.00007004519,0.0001441851,0.00005398455,0.005223134],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.03894417,"threshold_uncertainty_score":0.09577233,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08603744249388633,"score_gpt":0.3284766530567009,"score_spread":0.2424392105628145,"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."}}