{"id":"W6950310580","doi":"10.5281/zenodo.5687120","title":"Ephippiochthonius tetrachelatus Preyssler 1790, n. comb.","year":2017,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Computability, Logic, AI Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Carapace; Czech; Extant taxon; Femur; High resolution; Pathognomonic","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":"codex-gemma-dda1882f352a","candidate_categories":["sts","scholarly_communication","open_science","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.001191199,0.0002265621,0.0002268357,0.0001992567,0.005492119,0.00444822,0.006310849,0.0001065139,0.002600434],"category_scores_gemma":[0.001274604,0.0002342579,0.0001000939,0.0003487534,0.0003543395,0.001067632,0.00673118,0.000454797,0.008594112],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001765544,"about_ca_system_score_gemma":0.000008981093,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002896282,"about_ca_topic_score_gemma":5.262831e-7,"domain_scores_codex":[0.9972189,0.0003682902,0.0003266314,0.0008460646,0.0006229809,0.0006171233],"domain_scores_gemma":[0.9961811,0.0000560508,0.0002306864,0.002559932,0.0006603666,0.0003119058],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00003311052,0.0005766574,0.0001177842,0.00008284456,0.00008561155,0.0001073867,0.002110921,0.0002057968,0.001980975,0.05303215,0.1157209,0.8259459],"study_design_scores_gemma":[0.0007827549,0.0002889929,0.02353898,0.00002825284,0.00001156469,0.0002549146,0.00004773247,0.0152227,0.0006780878,0.006191469,0.9525086,0.000445964],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.03620064,0.0002720642,0.4619818,0.006581855,0.001418792,0.001419334,0.0001143222,0.004679232,0.487332],"genre_scores_gemma":[0.9860813,0.00003329777,0.01097584,0.0001828684,0.0002770164,8.026122e-8,0.000114915,0.0007911159,0.001543586],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9498807,"threshold_uncertainty_score":0.9990655,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04971995254509561,"score_gpt":0.266210400654678,"score_spread":0.2164904481095824,"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."}}