{"id":"W6962973404","doi":"10.17632/fkjzfkhbd5","title":"Downtown Recovery and Polycentricity Quotients for US and Canadian Downtowns","year":2023,"lang":"en","type":"dataset","venue":"Mendeley Data","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Downtown; Polycentricity; Quotient; Economic base analysis; Amazon rainforest; Skyline","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001367695,0.0008345111,0.0009187275,0.001189276,0.000509764,0.0005303744,0.002283273,0.0006718289,0.0002641753],"category_scores_gemma":[0.001381732,0.0008520661,0.00006757484,0.0006678871,0.0002009166,0.0007448631,0.002369685,0.0006479521,0.0028086],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006443145,"about_ca_system_score_gemma":0.001094175,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.7854832,"about_ca_topic_score_gemma":0.9201682,"domain_scores_codex":[0.9947216,0.0001606952,0.0006789335,0.00222898,0.0006322776,0.00157752],"domain_scores_gemma":[0.9938377,0.0003629162,0.000388426,0.004022903,0.0001198201,0.001268267],"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":[0.000132071,0.0000511905,0.0002156691,0.0002888371,0.0003177288,0.00008194597,0.00001027245,0.000001122991,0.000004424212,0.000004098608,0.9964173,0.002475359],"study_design_scores_gemma":[0.001295233,0.000128069,0.001366951,0.0001681877,0.0005409464,0.00004898148,0.00003046031,0.0001855595,0.000002790812,0.00007836036,0.995244,0.0009105197],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0002688751,0.001017334,0.000004566256,0.0002348174,0.001333567,0.001697211,0.9952051,0.0001351922,0.0001033481],"genre_scores_gemma":[0.00003289356,0.002447886,0.0002080502,0.0004373184,0.0004136861,0.0001087374,0.9954584,0.0002005098,0.000692583],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.134685,"threshold_uncertainty_score":0.999393,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.066961910345615,"score_gpt":0.3056473075612037,"score_spread":0.2386853972155887,"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."}}