{"id":"W4394091416","doi":"10.6084/m9.figshare.21299382","title":"Additional file 4 of Comparative transcriptomics analysis pipeline for the meta-analysis of phylogenetically divergent datasets (CoRMAP)","year":2022,"lang":"en","type":"dataset","venue":"Open MIND","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Pipeline (software); Transcriptome; Computational biology; Computer science; Meta-analysis; Biology; Data mining; Genetics; Gene; Programming language","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":[0.002313998,0.002057666,0.002051415,0.00319891,0.00137959,0.002326228,0.003093501,0.001666142,0.4629091],"category_scores_gemma":[0.008987654,0.0008536767,0.001367472,0.004949972,0.0005337585,0.001527393,0.001689499,0.001984308,0.1299209],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001255015,"about_ca_system_score_gemma":0.002442166,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007631947,"about_ca_topic_score_gemma":0.01715111,"domain_scores_codex":[0.9989899,0.0001591966,0.0001178164,0.0003719134,0.0001911374,0.0001700626],"domain_scores_gemma":[0.9956232,0.002712461,0.0002885814,0.0004813295,0.0006193976,0.0002751096],"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":[0.00026983,0.0001162601,0.003250925,0.004163377,0.000158229,0.00008055579,0.0001126056,0.0009323125,0.001211819,0.001103747,0.9836807,0.004919573],"study_design_scores_gemma":[0.001691524,0.0001229975,0.01508548,0.001080732,0.0002782464,0.0003158545,0.0002471723,0.001645948,0.002211122,0.006624552,0.9705658,0.000130676],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00009284102,0.0000176822,0.0001832852,0.00001919777,0.00000842118,0.00002085978,0.999132,0.0003100808,0.0002156625],"genre_scores_gemma":[0.0008907599,0.00003481933,0.001426472,0.00008824009,0.0000078932,0.0003152775,0.9960577,0.0003715177,0.0008074447],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.4629091,"threshold_uncertainty_score":0.7660949,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1078610555456245,"score_gpt":0.3377220895239444,"score_spread":0.2298610339783199,"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."}}