{"id":"W6911611980","doi":"10.5281/zenodo.13166591","title":"Syntormon flexibilis Becker 1922","year":2020,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Ichthyology and Marine Biology","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"China; Government (linguistics); Natural (archaeology); Period (music); Subject (documents)","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":[0.0002369902,0.0007330792,0.0003531446,0.002773117,0.001915631,0.0003389218,0.0005307136,0.0007277241,0.01665413],"category_scores_gemma":[0.0006832074,0.0002713939,0.0001457366,0.001267684,0.0006630181,0.0009422711,0.001304404,0.0006746131,0.003198385],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007510683,"about_ca_system_score_gemma":0.0003069596,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003347361,"about_ca_topic_score_gemma":0.005621371,"domain_scores_codex":[0.9996811,0.00003905693,0.00002026564,0.00008847041,0.0001124401,0.00005856936],"domain_scores_gemma":[0.9997894,0.00005803745,0.00006638927,0.00002937696,0.00002795942,0.00002876692],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00178661,0.0004048639,0.06238716,0.001097746,0.0002061392,0.01187408,0.004043282,0.004308235,0.1911312,0.02103453,0.02015158,0.6815745],"study_design_scores_gemma":[0.0002309555,0.0005005052,0.5123116,0.0007371631,0.0001953672,0.01898739,0.002178176,0.001997021,0.02749998,0.006059126,0.4291342,0.0001683872],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.696188,0.003913507,0.007282174,0.0005486155,0.0004891359,0.0002401542,0.003803929,0.0007141613,0.2868203],"genre_scores_gemma":[0.9593179,0.0009678386,0.002693874,0.0002108689,0.0001146356,0.00009485347,0.001795534,0.00003464856,0.03476986],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01665413,"threshold_uncertainty_score":0.05571365,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03193686697745131,"score_gpt":0.2281778364558062,"score_spread":0.196240969478355,"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."}}