{"id":"W4393270296","doi":"10.21203/rs.3.rs-3967452/v1","title":"tracerDB: A crowdsourced fluorescent tracer database for target engagement analysis","year":2024,"lang":"en","type":"preprint","venue":"Research Square","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Genentech; Deutschen Konsortium für Translationale Krebsforschung; Ontario Genomics Institute; European Federation of Pharmaceutical Industries and Associations; Merck KGaA; Ontario Genomics; Genome Canada; McGill University; Bayer; Deutsche Forschungsgemeinschaft; Deutsches Krebsforschungszentrum; Bristol-Myers Squibb","keywords":"TRACER; Fluorescence; Data science; Computer science; Database; Physics; Nuclear physics; Optics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.001005071,0.001303339,0.00137536,0.003802657,0.001000836,0.001693932,0.002392248,0.00166566,0.01528122],"category_scores_gemma":[0.004976904,0.0004923998,0.0008564721,0.00373711,0.0003710041,0.001500056,0.002355747,0.0009574021,0.01470904],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001183883,"about_ca_system_score_gemma":0.002260943,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0416053,"about_ca_topic_score_gemma":0.05415535,"domain_scores_codex":[0.9989408,0.000163074,0.00006936611,0.0002922481,0.0004195866,0.000114907],"domain_scores_gemma":[0.9978288,0.0004584023,0.0001329157,0.0007853853,0.0005253172,0.0002692724],"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":[0.001300466,0.0003955761,0.009480973,0.001295265,0.0003251035,0.0004363625,0.0009683493,0.03036296,0.01084446,0.01042537,0.7832919,0.1508732],"study_design_scores_gemma":[0.0004420195,0.0002306456,0.01232943,0.0002807498,0.0001626904,0.0003132462,0.001397926,0.2089119,0.01858672,0.03772961,0.7193182,0.0002969701],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"software","genre_scores_codex":[0.02279784,0.001092818,0.1156884,0.0004943917,0.0004144094,0.001076572,0.7302081,0.1017344,0.02649309],"genre_scores_gemma":[0.1396353,0.0007572828,0.1199126,0.0004000829,0.000103722,0.001677928,0.7167531,0.004118836,0.01664111],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.0416053,"threshold_uncertainty_score":0.08272624,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08235980012634743,"score_gpt":0.3946864396113714,"score_spread":0.312326639485024,"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."}}