{"id":"W6912931907","doi":"10.5683/sp3/a1bfnc","title":"ODRC data schema: Conformation weight","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":"Schema (genetic algorithms); Raw data; Data collection; Body weight; Documentation; Research data","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":[0.001409523,0.00222069,0.001396509,0.00305557,0.001030174,0.003188743,0.003456653,0.002034494,0.1040541],"category_scores_gemma":[0.005301497,0.001042222,0.001741742,0.006747511,0.000558017,0.00325112,0.002503924,0.002006228,0.1248602],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002946175,"about_ca_system_score_gemma":0.00376534,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06346599,"about_ca_topic_score_gemma":0.1072955,"domain_scores_codex":[0.9982411,0.0001486524,0.0002683693,0.0005766248,0.0005675086,0.0001978003],"domain_scores_gemma":[0.9967938,0.0004168384,0.0002226941,0.0009788801,0.001388884,0.0001989841],"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":[0.00007040445,0.00002912163,0.001477065,0.0004870571,0.00002129997,0.000024673,0.00005289726,0.0003800162,0.0004054486,0.001533508,0.9906848,0.004833627],"study_design_scores_gemma":[0.00003511411,0.000008578887,0.002183897,0.000132225,0.0000124265,0.00005589177,0.0001000687,0.000323274,0.0005192208,0.001031365,0.9955734,0.00002464651],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001380846,0.00003720239,0.0004276262,0.00006833038,0.00003355521,0.00001899246,0.9968437,0.0007770525,0.001655433],"genre_scores_gemma":[0.0004078403,0.00004722743,0.0007928182,0.00005017141,0.000003354582,0.00005091821,0.9975857,0.0001903519,0.0008715774],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1040541,"threshold_uncertainty_score":0.3480955,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04498593969814609,"score_gpt":0.3137867961090073,"score_spread":0.2688008564108612,"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."}}