{"id":"W1975339447","doi":"10.9708/jksci.2011.16.3.175","title":"A Similarity-based Inference System for Identifying Insects in the Ubiquitous Environments","year":2011,"lang":"en","type":"article","venue":"Journal of the Korea Society of Computer and Information","topic":"Diverse Approaches in Healthcare and Education Studies","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Sciencetech (Canada)","funders":"","keywords":"Identification (biology); Observational study; Inference; Insect; Similarity (geometry); Biodiversity; Artificial intelligence; Computer science; Machine learning; Ecology; Biology; Mathematics","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":[0.0005578024,0.00004635664,0.0001269815,0.0000313106,0.00007523858,0.00001363539,0.00009477164,0.00003468068,5.433243e-7],"category_scores_gemma":[0.00001586479,0.00002534654,0.0001067993,0.00006336159,0.00004743621,0.0003499572,0.00002740936,0.0001030565,2.490436e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004158674,"about_ca_system_score_gemma":0.00005635493,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001745033,"about_ca_topic_score_gemma":2.924994e-7,"domain_scores_codex":[0.9993812,0.00002646898,0.0003286072,0.00002448547,0.0001764355,0.00006279277],"domain_scores_gemma":[0.9994246,0.00007738946,0.0003211129,0.00007943394,0.00007839602,0.00001908042],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"observational","study_design_scores_codex":[0.0007678624,0.000766494,0.2301418,0.009691904,0.0006636771,0.000001676534,0.6485794,0.0006533696,0.0001357645,0.004152108,0.01780888,0.08663709],"study_design_scores_gemma":[0.002994921,0.0006892326,0.9375361,0.001101351,0.0001897255,0.00009064899,0.03281495,0.01811489,0.001501439,0.0005172002,0.004335192,0.00011432],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9462464,0.0000856649,0.05097608,0.001434935,0.0004805774,0.0004432786,0.000003854719,0.000003707127,0.0003254882],"genre_scores_gemma":[0.9892425,0.00006403003,0.00932857,0.001294022,0.00006220624,0.000004120539,0.000001683989,0.000001345135,0.000001554381],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7073943,"threshold_uncertainty_score":0.1033602,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1127576714570798,"score_gpt":0.3092813850763222,"score_spread":0.1965237136192424,"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."}}