{"id":"W6942401759","doi":"10.1371/journal.pone.0307138.t006","title":"The accuracy, precision values, recall values, and F1-score values comparison according to different state-of-the-art methods of the Toronto-3D dataset.","year":2024,"lang":"en","type":"dataset","venue":"Figshare","topic":"Mycorrhizal Fungi and Plant Interactions","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Recall; Precision and recall; Pattern recognition (psychology); Accuracy and precision; Standard deviation","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002543976,0.006752255,0.003478504,0.006465,0.001977222,0.003261687,0.005252108,0.004286997,0.02747305],"category_scores_gemma":[0.008896623,0.0008383045,0.003592952,0.005269431,0.001102703,0.001889458,0.002697602,0.002035472,0.04626688],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003511342,"about_ca_system_score_gemma":0.003931288,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1020124,"about_ca_topic_score_gemma":0.2635644,"domain_scores_codex":[0.9955786,0.0006150699,0.0004601614,0.001324274,0.001581588,0.0004403998],"domain_scores_gemma":[0.9963607,0.000958776,0.0002026797,0.0008619851,0.001403305,0.0002125178],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001973722,0.00009171379,0.001933291,0.002126179,0.0002101954,0.00006672334,0.00004301931,0.001605611,0.0008524931,0.0004273396,0.9742823,0.01816379],"study_design_scores_gemma":[0.0005682205,0.000128645,0.01540974,0.001166367,0.0005579919,0.0005711208,0.0003013429,0.01194858,0.005996387,0.00216723,0.9609233,0.0002611402],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.001664759,0.001844275,0.001076928,0.0001936692,0.0002480145,0.00008510747,0.9879497,0.004258114,0.002679433],"genre_scores_gemma":[0.001568125,0.0002135467,0.002025974,0.00006215167,0.00001597812,0.0001069654,0.9946854,0.0002476441,0.001074155],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1020124,"threshold_uncertainty_score":0.2028371,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05453431840847159,"score_gpt":0.3386593537755295,"score_spread":0.2841250353670579,"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."}}