{"id":"W6940082471","doi":"10.6084/m9.figshare.26600323","title":"Additional file 12 of MetaPro: a scalable and reproducible data processing and analysis pipeline for metatranscriptomic investigation of microbial communities","year":2024,"lang":"en","type":"dataset","venue":"Figshare","topic":"Mycorrhizal Fungi and Plant Interactions","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; SickKids Foundation; Hospital for Sick Children","funders":"","keywords":"Pipeline (software); Table (database); Data processing; Scalability; Stage (stratigraphy); Data file; File format; Pipeline transport","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":[0.002265212,0.001999243,0.001490953,0.002892704,0.001088142,0.002426344,0.003425141,0.002280117,0.366689],"category_scores_gemma":[0.01230122,0.0009184644,0.001585221,0.004691816,0.0005794333,0.001809997,0.001850475,0.002076935,0.1376965],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001606765,"about_ca_system_score_gemma":0.002780128,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01267598,"about_ca_topic_score_gemma":0.02364954,"domain_scores_codex":[0.9986969,0.0002080905,0.000183266,0.0004735968,0.0002392915,0.0001989763],"domain_scores_gemma":[0.9941444,0.003081179,0.0005144542,0.0009481772,0.0009158626,0.0003959657],"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":[0.0001697983,0.00005233636,0.002245879,0.001780179,0.00007784231,0.00003720378,0.00003474735,0.0005133791,0.0002413113,0.0005666697,0.9915241,0.002756489],"study_design_scores_gemma":[0.001603215,0.00008103829,0.01144283,0.001064779,0.0001322574,0.0001624919,0.000156357,0.001017001,0.001214174,0.004386669,0.978641,0.00009834638],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00005222062,0.00001128798,0.00007066542,0.00002493813,0.00000794663,0.000009766882,0.99949,0.000179773,0.0001533934],"genre_scores_gemma":[0.0005909507,0.00002600138,0.0006306338,0.00007006688,0.000008347437,0.0001565704,0.9978411,0.0001524346,0.0005238714],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.366689,"threshold_uncertainty_score":0.9033413,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07059927858508383,"score_gpt":0.2550869744823589,"score_spread":0.1844876958972751,"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."}}