{"id":"W6955554451","doi":"10.5883/ds-bicnp18","title":"BIOBUS INVENTORY OF CANADA`S NATIONAL PARKS 2008-2012 - INSECT ORDERS PART 6 - SMALL ORDERS","year":2014,"lang":"en","type":"dataset","venue":"Barcode of Life Data Systems","topic":"Prenatal Screening and Diagnostics","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Order (exchange); Work (physics); Measure (data warehouse); Production (economics)","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":[0.0006926463,0.002147115,0.00123076,0.007268917,0.001365319,0.001943545,0.002196874,0.001049976,0.02304944],"category_scores_gemma":[0.003941142,0.0009630182,0.001254758,0.01727884,0.0005126675,0.0007273916,0.0009884608,0.001585739,0.01492031],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01224368,"about_ca_system_score_gemma":0.02927072,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9420566,"about_ca_topic_score_gemma":0.9639223,"domain_scores_codex":[0.998998,0.00004543192,0.00007458559,0.0001950117,0.0004301205,0.0002568654],"domain_scores_gemma":[0.9962997,0.0003543021,0.0003365315,0.0003239452,0.00227239,0.0004131077],"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":[0.00007621211,0.00001651697,0.006199943,0.0004123516,0.00007194063,0.0000265406,0.00003654971,0.0006380091,0.00009582634,0.0004893848,0.9883626,0.003574132],"study_design_scores_gemma":[0.0001176947,0.000009336156,0.06339765,0.0004474874,0.00008377368,0.00005889534,0.0001800413,0.000974644,0.000454436,0.0005625134,0.933659,0.00005444093],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.000251781,0.000074379,0.00002727731,0.00002324956,0.000007706968,0.000004853229,0.9990748,0.0000782713,0.0004575905],"genre_scores_gemma":[0.0009409832,0.0001202075,0.000191336,0.00002904747,0.000003675905,0.00002798492,0.997609,0.00002778557,0.001049904],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.0579434,"threshold_uncertainty_score":0.1165692,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07700739733194013,"score_gpt":0.2723544144207057,"score_spread":0.1953470170887656,"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."}}