{"id":"W2763884763","doi":"10.1109/ihtc.2017.8058206","title":"Exploring anthropogenic activities and management decisions using a novel environmental agent based model","year":2017,"lang":"en","type":"article","venue":"","topic":"Aquatic Invertebrate Ecology and Behavior","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Coregonus clupeaformis; Population; Computer science; Agent-based model; Overhead (engineering); Fish <Actinopterygii>; Environmental resource management; Operations research; Fishery; Environmental science; Artificial intelligence; Engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0003398638,0.000641098,0.0006693281,0.0005671986,0.0006813828,0.001678418,0.001870208,0.001990205,0.003852127],"category_scores_gemma":[0.001192107,0.0004878304,0.0008569105,0.0006512139,0.00066777,0.0008672006,0.001337829,0.001067629,0.0002937207],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001011773,"about_ca_system_score_gemma":0.00155309,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04889532,"about_ca_topic_score_gemma":0.03809855,"domain_scores_codex":[0.9998275,0.00004850301,0.0000105443,0.00004238278,0.00003360866,0.000037454],"domain_scores_gemma":[0.9994783,0.0002560021,0.00008681712,0.000017717,0.00007996182,0.00008124041],"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":[0.00001075069,0.00001784014,0.0009667219,0.000008132214,0.0000142696,0.00004933682,0.00001627471,0.996103,0.0001242439,0.001570002,0.0001776924,0.0009417458],"study_design_scores_gemma":[0.000007143937,0.000005395778,0.000127318,0.000001651613,0.00000533726,0.000004258085,0.000009543721,0.9991197,0.00001607023,0.0004497951,0.0002510411,0.000002659452],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4378577,0.0008269344,0.5046802,0.003545893,0.0002899797,0.0002399229,0.002438326,0.0007715712,0.04934955],"genre_scores_gemma":[0.9375541,0.0004138134,0.0476491,0.0003105437,0.00008158095,0.0003209169,0.0008305004,0.00006410498,0.01277533],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.04889532,"threshold_uncertainty_score":0.09722143,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2128951505564961,"score_gpt":0.2963127212992302,"score_spread":0.08341757074273409,"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."}}