{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002822359,0.0001947079,0.0002622325,0.0003086816,0.00007107903,0.0001257887,0.001885248,0.0001360938,0.00002721531],"category_scores_gemma":[0.00003037043,0.000227646,0.0001425951,0.0006950955,0.00006227267,0.0005563885,0.003739637,0.0002427105,0.0000692535],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008689571,"about_ca_system_score_gemma":0.0001609317,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003015547,"about_ca_topic_score_gemma":0.00005501262,"domain_scores_codex":[0.9986101,0.00006567075,0.0001896233,0.0007495798,0.0001602322,0.0002248245],"domain_scores_gemma":[0.998415,0.0000258856,0.0002205303,0.00108638,0.0001806111,0.00007160073],"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":[0.00003248879,0.0001309353,0.001783376,0.0003353771,0.000296993,0.000374714,0.0003895423,0.4638551,0.00001541955,0.5051917,0.009110744,0.01848355],"study_design_scores_gemma":[0.0004885137,0.0000652563,0.002802437,0.0001031107,0.00007745281,4.192215e-7,0.00004132373,0.9234521,0.0002301764,0.0679886,0.004335108,0.0004155252],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005915608,0.00001067225,0.990474,0.0002303323,0.001082437,0.0002092608,0.0001276746,0.0003026176,0.001647406],"genre_scores_gemma":[0.9912867,0.0001730659,0.004394575,0.00006542898,0.00009967496,6.700798e-7,0.0001589211,0.00001774499,0.003803224],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9860794,"threshold_uncertainty_score":0.9283136,"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."}}