{"id":"W6926000235","doi":"10.18738/t8/ejonhj/qbc4kj","title":"IMGEO2_2018153_DEV_JKB2t_Y63a.txt","year":2024,"lang":"en","type":"dataset","venue":"Texas Digital Library (University of Texas)","topic":"Microbial Natural Products and Biosynthesis","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Process (computing); Identification (biology); Product (mathematics)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001032396,0.003938184,0.001841666,0.003204725,0.001024627,0.003528574,0.004063344,0.003529429,0.1609713],"category_scores_gemma":[0.004817978,0.001009122,0.001952159,0.004719274,0.0006234563,0.001847145,0.00260473,0.002274313,0.259003],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001652328,"about_ca_system_score_gemma":0.00210373,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01931491,"about_ca_topic_score_gemma":0.03401286,"domain_scores_codex":[0.9991331,0.0001405928,0.00006800712,0.0003013333,0.0001807006,0.0001763521],"domain_scores_gemma":[0.9985989,0.0004309131,0.0001171863,0.0003496191,0.0002762233,0.0002272543],"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":[0.00005618152,0.00001596381,0.0002357433,0.0003676454,0.00001829063,0.00001444769,0.000009471876,0.000204642,0.00007794151,0.0002188022,0.9976757,0.001105064],"study_design_scores_gemma":[0.0004501127,0.00003438234,0.001697179,0.000247345,0.00003479941,0.00007130155,0.000057317,0.0008420753,0.0005333471,0.001393554,0.9946027,0.00003591749],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00009804542,0.00008028284,0.00005695714,0.00008076973,0.00004024751,0.000008729989,0.9977043,0.0009843794,0.0009462581],"genre_scores_gemma":[0.0002246877,0.0000586734,0.000186081,0.00006428183,0.000009584063,0.00003928076,0.998454,0.0001474643,0.0008160018],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.8390287,"threshold_uncertainty_score":0.5385028,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009209091733280945,"score_gpt":0.191131964549198,"score_spread":0.181922872815917,"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."}}