{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007737769,0.0001931287,0.0002633508,0.0002629647,0.001066576,0.0002190976,0.0005798858,0.0002536083,0.0003463657],"category_scores_gemma":[0.00009538703,0.0002177172,0.0001668918,0.001006798,0.0005556147,0.0003822407,0.0002491972,0.0004989396,0.0001639671],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001334818,"about_ca_system_score_gemma":0.0008192554,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.01752395,"about_ca_topic_score_gemma":0.237088,"domain_scores_codex":[0.9981831,0.0004273675,0.000137796,0.0007387991,0.0002016025,0.0003113703],"domain_scores_gemma":[0.9988255,0.0002056825,0.0001671944,0.0004412384,0.0001925849,0.0001677878],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00003945741,0.00006110682,0.01357152,0.00004868282,0.0002791994,0.0001080585,0.006042792,0.4848577,0.000002186583,0.4939843,0.000988759,0.00001623683],"study_design_scores_gemma":[0.001203573,0.0001185974,0.003565188,0.0004224714,0.00135241,4.854364e-7,0.07921465,0.2242702,0.00003986136,0.6852475,0.002724275,0.001840846],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.217913,0.00001699313,0.7709207,0.001284075,0.000300504,0.0004021908,0.00003715927,0.0003504635,0.008774944],"genre_scores_gemma":[0.9875592,0.0001396403,0.00004201906,0.0001227366,0.0001618179,0.00000149667,0.00004223267,0.00001684759,0.01191401],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7708787,"threshold_uncertainty_score":0.9890184,"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."}}