{"id":"W20412664","doi":"10.1385/1-59745-187-8:127","title":"Protein Library Design and Screening: Working Out the Probabilities","year":2006,"lang":"en","type":"article","venue":"Humana Press eBooks","topic":"Monoclonal and Polyclonal Antibodies Research","field":"Medicine","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; HEC Montréal","funders":"","keywords":"Computer science; Information retrieval; Computational biology; Data science; Biology","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":[],"consensus_categories":[],"category_scores_codex":[0.0001933732,0.0001434557,0.0001754958,0.00004498352,0.0002549754,0.0001151637,0.0001396196,0.00005500164,0.00004525615],"category_scores_gemma":[0.0000135479,0.00008626879,0.00005828636,0.00001364986,0.000336826,0.00005516653,0.0001642588,0.000246703,0.000004699465],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00000627148,"about_ca_system_score_gemma":0.00003651957,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002668833,"about_ca_topic_score_gemma":0.00001492025,"domain_scores_codex":[0.9988773,0.0001177085,0.0001897688,0.0002271235,0.0002883962,0.0002997268],"domain_scores_gemma":[0.9994928,0.0001346562,0.00004197863,0.0002309586,0.0000250751,0.00007459263],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.003753536,0.0003111172,0.005608566,0.001615502,0.0004580187,0.0004786733,0.005134428,0.00007565316,0.008036389,0.5352369,0.04649531,0.3927959],"study_design_scores_gemma":[0.00108736,0.00038373,0.007242441,0.0005701397,0.00007059695,0.00006208333,0.0001255547,0.001380769,0.03106191,0.01649055,0.941188,0.0003368044],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.3268069,0.02079643,0.002021072,0.003501997,0.0001017968,0.005431256,0.00001092129,0.0004369378,0.6408927],"genre_scores_gemma":[0.6861343,0.00006291223,0.007657808,0.0003328827,0.000727325,0.000283397,0.00001251673,0.00004645548,0.3047423],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8946928,"threshold_uncertainty_score":0.3517939,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.135786950947358,"score_gpt":0.2957961069562562,"score_spread":0.1600091560088982,"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."}}