{"id":"W4394036425","doi":"10.5281/zenodo.10044724","title":"zol: prepTG Databases for BGC-rich Taxa","year":2023,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Scientific Computing and Data Management","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Database; Taxon; Computer science; Geography; Biology; Paleontology","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":[],"consensus_categories":[],"category_scores_codex":[0.0008829546,0.002503333,0.001602333,0.004604989,0.0008824646,0.002141562,0.003679082,0.001895826,0.06344382],"category_scores_gemma":[0.003532341,0.0007840908,0.001379603,0.007091259,0.0004978649,0.001666333,0.00224706,0.002139986,0.08726805],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001292327,"about_ca_system_score_gemma":0.00188091,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007813933,"about_ca_topic_score_gemma":0.01472198,"domain_scores_codex":[0.9990901,0.000105929,0.0001284171,0.0003375249,0.0001841632,0.0001539701],"domain_scores_gemma":[0.9990004,0.0002187732,0.000145631,0.0003168204,0.0002058325,0.0001126129],"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":[0.0002299846,0.00005284835,0.001472263,0.0017718,0.00006800127,0.00007605823,0.00006115713,0.0007069979,0.001140238,0.00135072,0.9887866,0.004283344],"study_design_scores_gemma":[0.0003309084,0.00003500832,0.003689151,0.0003072075,0.00005376654,0.0001720843,0.0001370713,0.0008582447,0.00177524,0.002317534,0.9902829,0.00004097738],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0002551714,0.00006733372,0.0001220658,0.00002703551,0.00001285328,0.0000117322,0.9983077,0.0007674997,0.0004284795],"genre_scores_gemma":[0.0003094404,0.00003751976,0.0004113129,0.00002289689,0.000002168821,0.00004409645,0.9988979,0.0001097362,0.0001649614],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.06344382,"threshold_uncertainty_score":0.2122407,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2590204264340215,"score_gpt":0.3890560244548669,"score_spread":0.1300355980208455,"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."}}