{"id":"W4380135931","doi":"10.4230/lipics.giscience.2023.41","title":"Understanding Place Identity with Generative AI","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Generative grammar; Identity (music); Generative model; Set (abstract data type); Computer science; Perception; Trustworthiness; Generative Design; Data science; Artificial intelligence; Human–computer interaction; Psychology; Engineering; Internet privacy; Aesthetics","routes":{"ca_aff":true,"ca_fund":false,"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":[0.002072632,0.0006104082,0.0003971517,0.002352291,0.0007398811,0.003963162,0.001229106,0.0008401273,0.004328669],"category_scores_gemma":[0.01439956,0.000489497,0.00137324,0.001536478,0.002896033,0.004169133,0.003177733,0.001239629,0.0005915355],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001619192,"about_ca_system_score_gemma":0.0007604,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008768426,"about_ca_topic_score_gemma":0.008636099,"domain_scores_codex":[0.9987166,0.0007414398,0.0000457599,0.0002728068,0.0001578975,0.00006558304],"domain_scores_gemma":[0.9920098,0.005873652,0.0004190105,0.001247323,0.0002980825,0.0001521953],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009987419,0.0001552781,0.03931946,0.0003696401,0.0002838933,0.0003570993,0.01205504,0.2487796,0.002097402,0.571881,0.003403851,0.121198],"study_design_scores_gemma":[0.00001422072,0.00002834328,0.00497947,0.0001140235,0.00003535049,0.0001691901,0.002654346,0.4821627,0.0007295033,0.4998884,0.009184741,0.00003971555],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1162203,0.000494196,0.8555135,0.001871942,0.00007254002,0.0001207713,0.0006203745,0.0006396445,0.02444679],"genre_scores_gemma":[0.8594894,0.0002296375,0.1373604,0.0002162256,0.00002783405,0.0001554023,0.0007460926,0.0001069186,0.001668035],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008768426,"threshold_uncertainty_score":0.01743478,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2955564935859102,"score_gpt":0.2625977165187391,"score_spread":0.0329587770671711,"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."}}