{"id":"W4387928372","doi":"10.48550/arxiv.2310.13098","title":"SRAI: Towards Standardization of Geospatial AI","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Geospatial analysis; Python (programming language); Computer science; Standardization; Geospatial PDF; Embedding; Data science; Database; Software engineering; Artificial intelligence; Geography; Programming language; Cartography; Operating system","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":[0.008925701,0.001507375,0.001084188,0.004999729,0.001172867,0.007650434,0.005499371,0.00159322,0.01637538],"category_scores_gemma":[0.02348913,0.001087423,0.001920545,0.007102667,0.002968524,0.009528208,0.01141896,0.008530083,0.01519605],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001681069,"about_ca_system_score_gemma":0.006483663,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003612416,"about_ca_topic_score_gemma":0.003220558,"domain_scores_codex":[0.9915884,0.002514886,0.001065477,0.001363779,0.0030621,0.0004052211],"domain_scores_gemma":[0.98634,0.002169994,0.0004861987,0.006618431,0.00345554,0.0009297047],"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":[0.00008946575,0.0001153433,0.0009081603,0.00077095,0.0001179001,0.00008962306,0.0007755072,0.00834456,0.003572092,0.6211586,0.1126627,0.2513951],"study_design_scores_gemma":[0.00002472828,0.00002770439,0.0007041384,0.0002782069,0.00003118217,0.000187067,0.0001657932,0.03870395,0.004206967,0.3123876,0.6432282,0.00005447128],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.000883889,0.0003699128,0.9685952,0.001018615,0.0004054148,0.0001203877,0.002579933,0.01648123,0.009545406],"genre_scores_gemma":[0.02216752,0.001364799,0.9340479,0.0009760552,0.0003685377,0.0007589863,0.01897679,0.0115948,0.009744643],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01637538,"threshold_uncertainty_score":0.05478102,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0898239813681688,"score_gpt":0.2047270249976949,"score_spread":0.1149030436295261,"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."}}