{"id":"W6930444582","doi":"10.5281/zenodo.13074345","title":"Source Code of GearBind: Pretrainable Geometric Graph Neural Network for Antibody Affinity Maturation","year":2023,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Marine animal studies overview","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Mila - Quebec Artificial Intelligence Institute","funders":"","keywords":"Graph; Artificial neural network; In silico; Source code; Pattern recognition (psychology); Antibody; Code (set theory)","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":[0.0007329286,0.004813429,0.001203274,0.00155413,0.0005319326,0.001336382,0.004050071,0.002540643,0.02560168],"category_scores_gemma":[0.002875183,0.001067654,0.001778148,0.00173618,0.0005010148,0.0009836883,0.001285751,0.002709488,0.03310583],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001973647,"about_ca_system_score_gemma":0.002172593,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02999053,"about_ca_topic_score_gemma":0.06492227,"domain_scores_codex":[0.999527,0.00006297135,0.0000363269,0.0001773792,0.0001272944,0.00006910261],"domain_scores_gemma":[0.9994661,0.0001735408,0.00003410453,0.0001506237,0.000133215,0.00004241387],"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":[0.000289669,0.000217163,0.00193199,0.001056388,0.0002034101,0.0001176447,0.00002488484,0.02591,0.001038037,0.0008437437,0.9336326,0.03473452],"study_design_scores_gemma":[0.002343274,0.0004421349,0.007878598,0.0004592161,0.0002022287,0.0006245651,0.0001099987,0.2420608,0.01223642,0.01772935,0.7157364,0.0001770746],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"software","genre_scores_codex":[0.006713734,0.001177744,0.01122947,0.0005622142,0.0003479822,0.0003433765,0.9337117,0.0409053,0.005008502],"genre_scores_gemma":[0.006616143,0.0002778088,0.01261311,0.0001898907,0.00001703145,0.0006182109,0.9760129,0.0008529265,0.002801907],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.02999053,"threshold_uncertainty_score":0.08564615,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03676126990482727,"score_gpt":0.2642459514601605,"score_spread":0.2274846815553332,"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."}}