{"id":"W2052582630","doi":"10.1145/1456223.1456358","title":"Using multi-agent geo-simulation techniques for intelligent sensor web management","year":2008,"lang":"en","type":"article","venue":"","topic":"Environmental Monitoring and Data Management","field":"Earth and Planetary Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"","keywords":"Computer science; Wireless sensor network; Sensor web; Distributed computing; Context (archaeology); Process (computing); Resource (disambiguation); Variety (cybernetics); Real-time computing; Key distribution in wireless sensor networks; Computer network; Wireless; Telecommunications; Artificial intelligence","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":[0.0001445467,0.0001240227,0.00009517163,0.00008443345,0.0002270779,0.00002826625,0.0001234981,0.00003406719,0.0003701752],"category_scores_gemma":[0.000004891043,0.000105412,0.00005222735,0.00007113971,0.00003912882,0.0001626189,0.00003067734,0.00003986315,0.0001361353],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001917532,"about_ca_system_score_gemma":0.000003683508,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003222412,"about_ca_topic_score_gemma":0.00004808112,"domain_scores_codex":[0.9991151,0.00001972176,0.0001901973,0.0002664293,0.0001822657,0.0002262764],"domain_scores_gemma":[0.9996322,0.00003740432,0.00004608552,0.000205957,0.000008380813,0.00007000587],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008074002,0.0001904409,0.5740879,0.0001344466,0.0001151589,0.0000627042,0.0001602269,0.2422804,0.0002195667,0.0001031172,0.000652095,0.1819132],"study_design_scores_gemma":[0.0004570198,0.0001646192,0.2677671,0.00003592823,0.00005617967,0.00001039671,0.0005015059,0.6335058,0.002570204,0.00005211521,0.09444697,0.0004321689],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4115353,0.0001688646,0.5771562,0.00006813985,0.0006632119,0.001556813,0.0001168292,0.0003061975,0.008428452],"genre_scores_gemma":[0.7970999,0.0001980027,0.2004167,0.0001079694,0.0000815817,0.000005149082,0.0001034879,0.000004570481,0.001982596],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.3912253,"threshold_uncertainty_score":0.4298575,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1085704072600785,"score_gpt":0.2963641181309207,"score_spread":0.1877937108708422,"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."}}