{"id":"W2053193099","doi":"10.1117/12.2082596","title":"Dynamic hierarchical algorithm for accelerated microfossil identification","year":2015,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Identification (biology); Computer science; Artificial neural network; Benchmark (surveying); Artificial intelligence; Computation; Unsupervised learning; Supervised learning; Machine learning; Pattern recognition (psychology); Algorithm; Geology; Biology","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":[0.001250345,0.0002805372,0.0004014459,0.0001300853,0.0001295682,0.000382207,0.001744045,0.0001690488,0.000003034596],"category_scores_gemma":[0.00060391,0.0002363297,0.0006329119,0.0005739024,0.0001691558,0.0009991862,0.0002843614,0.0002412528,0.000001937175],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001838549,"about_ca_system_score_gemma":0.00006667557,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008357726,"about_ca_topic_score_gemma":1.635986e-7,"domain_scores_codex":[0.997591,2.251275e-8,0.0008087724,0.0005129159,0.0006592012,0.0004280483],"domain_scores_gemma":[0.9962783,0.0001417702,0.0004918905,0.00009434716,0.002813827,0.0001798644],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006190797,0.0001542888,0.00006004252,0.0001903163,0.0004696883,9.219189e-8,0.0004595256,0.0001965246,0.4033394,0.5766627,0.00424579,0.01415971],"study_design_scores_gemma":[0.001020331,0.0002763366,0.0003261975,0.0001013693,0.0001069695,0.00001678174,0.0006739016,0.9328202,0.05507937,0.00514702,0.004089618,0.0003418873],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.9628391,0.0001285111,0.03294035,0.002578267,0.0003490111,0.0005322014,0.00004817764,0.0001250359,0.00045932],"genre_scores_gemma":[0.1531424,0.00004039078,0.8455434,0.0001012945,0.000316782,0.0002179514,0.00002661699,0.00005493118,0.0005562037],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9326237,"threshold_uncertainty_score":0.9637247,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01845003942462446,"score_gpt":0.2439668721552934,"score_spread":0.225516832730669,"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."}}