{"id":"W6913214582","doi":"10.5683/sp3/opmpl1","title":"OBRC data schema: Animal historical RFID data","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); Documentation; Raw data; Identification (biology); Data integration; Data collection; Conceptual schema; Linked data; Data access","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":[0.001748748,0.001670561,0.001139049,0.004014062,0.001187614,0.002660262,0.00329725,0.001988915,0.07928853],"category_scores_gemma":[0.006166704,0.0009963566,0.00124126,0.009114421,0.0005713214,0.002586778,0.002237513,0.001807179,0.1027127],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004013433,"about_ca_system_score_gemma":0.004955264,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1439236,"about_ca_topic_score_gemma":0.2269899,"domain_scores_codex":[0.9982637,0.0002009111,0.0002452111,0.0004670316,0.00056991,0.0002532817],"domain_scores_gemma":[0.9959324,0.0005445493,0.0002966694,0.001041612,0.001820166,0.0003646081],"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":[0.00005069681,0.00001996777,0.0008371961,0.0004078122,0.0000163674,0.00002149391,0.00005625374,0.0001888806,0.0002683071,0.0009232914,0.9946689,0.002540844],"study_design_scores_gemma":[0.00003544501,0.000006903166,0.002597038,0.0001595484,0.00001416527,0.00004789019,0.0001251319,0.0001763195,0.0003201981,0.000503255,0.9959924,0.00002178481],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00009982282,0.00003291726,0.0001691356,0.00005355864,0.00001984802,0.00001560027,0.9979125,0.0004028901,0.00129373],"genre_scores_gemma":[0.0002692322,0.0000316945,0.0004040236,0.00003279829,0.000002312957,0.00003974933,0.9985116,0.00007576595,0.000632817],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1439236,"threshold_uncertainty_score":0.2861717,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1324703867482099,"score_gpt":0.349545053752751,"score_spread":0.2170746670045411,"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."}}