{"id":"W6931676321","doi":"10.5683/sp3/yainmi","title":"ODRC data schema: Feed carts","year":2024,"lang":"en","type":"dataset","venue":"Borealis","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Raw data; Schema (genetic algorithms); Documentation; Cart; Data collection; Group method of data handling","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002697441,0.00169792,0.001152686,0.004687643,0.001076728,0.005372892,0.003156848,0.002446078,0.1710743],"category_scores_gemma":[0.009035966,0.001161915,0.001530186,0.009615967,0.0006106363,0.006074233,0.003185344,0.001961954,0.1513415],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003512867,"about_ca_system_score_gemma":0.003657057,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06383276,"about_ca_topic_score_gemma":0.05589843,"domain_scores_codex":[0.9971982,0.0003391196,0.0005375177,0.0006890569,0.00101895,0.0002170338],"domain_scores_gemma":[0.9933015,0.001165988,0.0003978783,0.001630209,0.003219183,0.0002852261],"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":[0.00008087186,0.00003312072,0.001132504,0.0006133145,0.00002530499,0.00005530778,0.0001968614,0.0005365367,0.0003633545,0.006664425,0.9767997,0.01349869],"study_design_scores_gemma":[0.00001243388,0.000003605286,0.0005578306,0.0001260907,0.000005220487,0.00003598171,0.0001120904,0.0001621236,0.000279676,0.001040128,0.9976494,0.00001563164],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0002826699,0.00008772043,0.004898701,0.0002621347,0.0001352651,0.0001001018,0.9740756,0.004706899,0.01545104],"genre_scores_gemma":[0.001981661,0.0002431071,0.007708671,0.0004298598,0.00002807666,0.0002366624,0.9791686,0.00271183,0.007491524],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1710743,"threshold_uncertainty_score":0.5723006,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05908933021984304,"score_gpt":0.3245388550487928,"score_spread":0.2654495248289498,"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."}}