{"id":"W6889015751","doi":"10.25345/c5j52z","title":"MassIVE MSV000087525 - MAGEL2- Full length and truncation Interactome","year":2021,"lang":"en","type":"dataset","venue":"UC San Diego","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Truncation (statistics); Interactome; Generalization; Term (time)","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0003351895,0.0007962521,0.0008703199,0.0006249846,0.0001592819,0.0003970763,0.0006350483,0.0005647578,0.009720315],"category_scores_gemma":[0.0007475291,0.0008165564,0.0001937789,0.0005158173,0.0002092273,0.0004124211,0.0006240193,0.001104221,0.01182558],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003906368,"about_ca_system_score_gemma":0.0002452334,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002342958,"about_ca_topic_score_gemma":0.00231365,"domain_scores_codex":[0.9964333,0.000372177,0.0006645534,0.001217315,0.0006713154,0.0006413511],"domain_scores_gemma":[0.9968359,0.0003465123,0.000633902,0.001635871,0.0002517414,0.0002960961],"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":[0.0001223784,0.000123475,0.00003605833,0.0002947124,0.0002270356,0.0003752203,0.0001066673,0.000001414782,0.0005691171,0.00001479295,0.9966313,0.001497872],"study_design_scores_gemma":[0.0007129886,0.0001270838,0.0007314053,0.0004002036,0.0003574782,0.000110522,0.000281968,0.00002511693,0.0002030363,0.00007445578,0.9961467,0.0008289933],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.002519235,0.001712598,0.000005751694,0.0000881372,0.001028691,0.0005102165,0.993335,0.0001536044,0.0006467775],"genre_scores_gemma":[0.0008702116,0.0003897595,0.0002247321,0.000225882,0.0008021147,0.0001047481,0.9964638,0.0001894206,0.0007293432],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.0031288,"threshold_uncertainty_score":0.9994285,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01551385721205748,"score_gpt":0.2769528868199961,"score_spread":0.2614390296079386,"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."}}