{"id":"W6929866369","doi":"10.5281/zenodo.10925218","title":"METIS case study record selection","year":2024,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Metis; Selection (genetic algorithm); Identification (biology); Record linkage","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":[0.002157182,0.002382838,0.001776362,0.01042292,0.001220998,0.002402373,0.003018,0.002692339,0.0322644],"category_scores_gemma":[0.01144748,0.0006610382,0.002442119,0.008354045,0.0006979679,0.0009733022,0.001670971,0.001586754,0.02500693],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001606015,"about_ca_system_score_gemma":0.005075014,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01994843,"about_ca_topic_score_gemma":0.03879235,"domain_scores_codex":[0.9966744,0.000699863,0.0006108794,0.0008427805,0.0007796453,0.0003923358],"domain_scores_gemma":[0.994472,0.001941053,0.0005113881,0.001321444,0.001373386,0.0003806358],"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":[0.0007481834,0.0003014677,0.008701519,0.003101177,0.0002539935,0.0008012481,0.0001016418,0.002215439,0.001055879,0.001517046,0.9561929,0.0250095],"study_design_scores_gemma":[0.001126237,0.0001935443,0.01178698,0.0008986162,0.000347084,0.001311591,0.0004718216,0.005561939,0.003351902,0.002161535,0.9726962,0.00009258014],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.006841346,0.0009163454,0.000858858,0.0003035551,0.0001535557,0.0004132205,0.986944,0.000725722,0.002843424],"genre_scores_gemma":[0.006670067,0.000343874,0.002559544,0.0001187773,0.00004190165,0.0007264402,0.9868736,0.0001179035,0.002547855],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.0322644,"threshold_uncertainty_score":0.1079351,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05279554286127391,"score_gpt":0.3201402952201534,"score_spread":0.2673447523588794,"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."}}