{"id":"W1977869532","doi":"10.17161/bi.v9i1.4611","title":"Character Selection During Interactive Taxonomic Identification: “Best Characters”","year":2014,"lang":"en","type":"article","venue":"Biodiversity Informatics","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Royal Ontario Museum","funders":"","keywords":"Computer science; Categorical variable; Python (programming language); Identification (biology); Software; Data mining; Variety (cybernetics); Selection (genetic algorithm); Transparency (behavior); Task (project management); Range (aeronautics); Machine learning; Information retrieval; Artificial intelligence; Programming language","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007644594,0.003285384,0.002054165,0.005478035,0.002334747,0.004948705,0.003291491,0.002108235,0.1339253],"category_scores_gemma":[0.03965091,0.001638669,0.00186278,0.003820375,0.001286086,0.006743881,0.006869501,0.002609256,0.0452627],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001104471,"about_ca_system_score_gemma":0.001982555,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00152198,"about_ca_topic_score_gemma":0.003099667,"domain_scores_codex":[0.9961562,0.001134693,0.0006120554,0.000949681,0.0007459964,0.0004013959],"domain_scores_gemma":[0.980164,0.01207185,0.001488734,0.002852692,0.00253669,0.0008860503],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00220288,0.0002100024,0.007685352,0.002509415,0.0002357429,0.0006112603,0.003104283,0.002254428,0.01454178,0.01575373,0.4992172,0.4516738],"study_design_scores_gemma":[0.0004981828,0.0002624356,0.01207015,0.001668793,0.0001787126,0.001300206,0.001350099,0.09205138,0.1072723,0.05897426,0.7237111,0.0006624648],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.009296556,0.0004951701,0.6000637,0.0006962331,0.0006189219,0.0004938232,0.01575344,0.3593296,0.01325247],"genre_scores_gemma":[0.05631686,0.0002706646,0.8174133,0.0007675808,0.0002030849,0.001321269,0.01402789,0.09958376,0.01009553],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1339253,"threshold_uncertainty_score":0.4480249,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01585584702534414,"score_gpt":0.1985125670486511,"score_spread":0.1826567200233069,"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."}}