{"id":"W1605009934","doi":"10.1111/tgis.12147","title":"Feature‐Driven Generalization of Isobaths on Nautical Charts: A Multi‐Agent System Approach","year":2015,"lang":"en","type":"article","venue":"Transactions in GIS","topic":"Maritime Navigation and Safety","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"","keywords":"Generalization; Nautical chart; Bathymetry; Feature (linguistics); Process (computing); Computer science; Chart; Set (abstract data type); Artificial intelligence; Marine engineering; Geography; Engineering; Cartography; Mathematics; Mathematical analysis","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":[0.000112692,0.00009468034,0.0001439104,0.0001127462,0.00002304959,0.000008560767,0.00006453876,0.0001027523,0.0000410724],"category_scores_gemma":[0.000005505004,0.00009658259,0.00004629886,0.0002402277,0.00001956594,0.00005997246,0.000001496032,0.00014934,0.00001976595],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001631698,"about_ca_system_score_gemma":0.00001529385,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002512133,"about_ca_topic_score_gemma":0.00001446137,"domain_scores_codex":[0.9993989,0.00003741202,0.0001778573,0.0001138834,0.0001559545,0.0001160013],"domain_scores_gemma":[0.9997149,0.00001303496,0.00001553405,0.0001439384,0.00004047063,0.00007211653],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003817097,0.0002925733,0.0002023803,0.000368526,0.00005323174,0.000005668032,0.002035361,0.9849731,0.0007790411,0.003999134,0.0008616056,0.006391232],"study_design_scores_gemma":[0.0009061221,0.0000281449,0.0006784123,0.00006349847,0.00001756973,0.00001088837,0.0003215334,0.9928458,0.001554856,0.000004849263,0.003444474,0.0001238713],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01449852,0.0001100898,0.97155,0.0001669221,0.0004355662,0.0003615003,0.0000618613,0.0003047851,0.01251075],"genre_scores_gemma":[0.9884672,0.0000168792,0.0109823,0.00001697684,0.00002653687,0.00004234159,0.0000331736,0.0000181267,0.0003964208],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9739687,"threshold_uncertainty_score":0.3938524,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03108768763671847,"score_gpt":0.245378787583074,"score_spread":0.2142910999463555,"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."}}