{"id":"W6962794661","doi":"10.17632/s662r5mkxx.1","title":"Why Resident Identification Matters","year":2023,"lang":"en","type":"dataset","venue":"Data Archiving and Networked Services (DANS)","topic":"Urban Green Space and Health","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Identification (biology); Data collection; Descriptive statistics; Descriptive research; Survey research; Population","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01055871,0.000201768,0.000411015,0.001463396,0.001390282,0.001672816,0.001335887,0.001200928,0.01325269],"category_scores_gemma":[0.08063392,0.0003846117,0.0005286323,0.004982558,0.0006011787,0.002503437,0.001802005,0.00130293,0.004041302],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00142223,"about_ca_system_score_gemma":0.003824939,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03560153,"about_ca_topic_score_gemma":0.05099105,"domain_scores_codex":[0.9882466,0.005156285,0.001696971,0.001621637,0.001861296,0.001417219],"domain_scores_gemma":[0.9622966,0.01540898,0.008560426,0.003981097,0.00762758,0.002125253],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002260712,0.0001060582,0.5084559,0.001299989,0.0001231265,0.0002147099,0.003611119,0.0001716024,0.0000963084,0.004432995,0.4136528,0.06760936],"study_design_scores_gemma":[0.00008576775,0.0001070936,0.6658295,0.00348058,0.0001366885,0.0007177057,0.01347563,0.0007193653,0.0003571945,0.003941345,0.3110863,0.00006272498],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"dataset","genre_scores_codex":[0.4029388,0.009984938,0.00730529,0.1720903,0.005009887,0.000943133,0.3293367,0.000360065,0.07203094],"genre_scores_gemma":[0.8148637,0.006502057,0.004690592,0.03735819,0.002052733,0.002631193,0.116526,0.000289238,0.0150863],"genre_candidate":"dataset","genre_consensus":null,"teacher_disagreement_score":0.03560153,"threshold_uncertainty_score":0.07078862,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02163953961590674,"score_gpt":0.2662834806577818,"score_spread":0.2446439410418751,"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."}}