{"id":"W4280498469","doi":"10.5281/zenodo.6566983","title":"Antibody Characterization Report for Retinoic acid receptor RXR-alpha","year":2022,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Monoclonal and Polyclonal Antibodies Research","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Montreal Neurological Institute and Hospital","funders":"","keywords":"Retinoic acid; Retinoic acid receptor alpha; Antibody; Alpha (finance); Retinoic acid receptor gamma; Retinoic acid receptor beta; Chemistry; Retinoid X receptor; Retinoic acid receptor; Biochemistry; Biology; Immunology; Medicine; Nuclear receptor; Transcription factor","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":[0.0009658848,0.001782399,0.0009534954,0.001943679,0.00116965,0.001062699,0.001472918,0.0007830883,0.02189332],"category_scores_gemma":[0.001727772,0.0008598927,0.001172861,0.001483742,0.0002366339,0.0008425267,0.0005367231,0.002019216,0.02299545],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000733295,"about_ca_system_score_gemma":0.00120896,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001565711,"about_ca_topic_score_gemma":0.002507542,"domain_scores_codex":[0.9988361,0.0001872246,0.0002071524,0.0002776519,0.0002813143,0.000210438],"domain_scores_gemma":[0.9983521,0.0003232076,0.0001399431,0.0002654147,0.0007473087,0.0001721292],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002754273,0.000165306,0.0003505872,0.0004337683,0.00005420413,0.0002406894,0.00007366147,0.00008980075,0.980456,0.0006702007,0.006059098,0.01113117],"study_design_scores_gemma":[0.0002207864,0.001018691,0.008919765,0.0002136697,0.0003336353,0.004751421,0.0001460692,0.001590205,0.5492388,0.0005046121,0.4330048,0.00005749783],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"dataset","genre_scores_codex":[0.3059685,0.05991099,0.4858762,0.004523953,0.004342173,0.005619171,0.05646852,0.003677516,0.07361303],"genre_scores_gemma":[0.223987,0.06540398,0.2673692,0.003572935,0.00219609,0.005347073,0.3371958,0.001517028,0.09341087],"genre_candidate":"dataset","genre_consensus":null,"teacher_disagreement_score":0.02189332,"threshold_uncertainty_score":0.07324046,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03684678028069014,"score_gpt":0.2967971394612219,"score_spread":0.2599503591805317,"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."}}