{"id":"W3016808950","doi":"10.3390/ijgi9040270","title":"Representing Complex Evolving Spatial Networks: Geographic Network Automata","year":2020,"lang":"en","type":"article","venue":"ISPRS International Journal of Geo-Information","topic":"Land Use and Ecosystem Services","field":"Environmental Science","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Social Sciences and Humanities Research Council of Canada; Natural Sciences and Engineering Research Council of Canada","keywords":"Geospatial analysis; Computer science; Geographic information system; Spatial network; Representation (politics); Theoretical computer science; Cellular automaton; Spatial analysis; Complex network; Automaton; Network formation; Data mining; Artificial intelligence; Geography; Cartography; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0009208149,0.0005306349,0.0004776933,0.001123538,0.0006214823,0.002767141,0.001514883,0.001241724,0.003061763],"category_scores_gemma":[0.005199096,0.0003065106,0.0008072131,0.001670159,0.001587518,0.00382857,0.001810157,0.00105389,0.0004080248],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001481191,"about_ca_system_score_gemma":0.001069703,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01067868,"about_ca_topic_score_gemma":0.008469308,"domain_scores_codex":[0.9992586,0.0003208767,0.00005105903,0.0001858458,0.00013764,0.00004594237],"domain_scores_gemma":[0.9980107,0.00116509,0.0002466164,0.0002834831,0.0002098739,0.00008429898],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00001870222,0.00001701744,0.001798764,0.00006748931,0.00003300289,0.0001435761,0.0004524638,0.6137418,0.0007419715,0.3656352,0.001168672,0.01618142],"study_design_scores_gemma":[0.00000603822,0.000008623867,0.0002054697,0.00001575142,0.0000132787,0.00005020962,0.0001123501,0.8581704,0.0002210861,0.1353292,0.005855863,0.00001171897],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02529608,0.0003613645,0.9639922,0.0005842117,0.00007067651,0.00005653974,0.0004557549,0.0003917524,0.008791481],"genre_scores_gemma":[0.7032086,0.001004233,0.2900801,0.000180266,0.00006427619,0.0003248554,0.0007185909,0.0001109579,0.004308159],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01067868,"threshold_uncertainty_score":0.02123302,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01001972416713878,"score_gpt":0.2229206684665085,"score_spread":0.2129009442993697,"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."}}