{"id":"W2108919989","doi":"10.1187/cbe.11-08-0071","title":"Visualizing Protein Interactions and Dynamics: Evolving a Visual Language for Molecular Animation","year":2012,"lang":"en","type":"article","venue":"CBE—Life Sciences Education","topic":"Genetics, Bioinformatics, and Biomedical Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":60,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Strong; National Science Foundation","keywords":"Animation; Visualization; Motion (physics); Dynamics (music); Event (particle physics); Computer science; Computational biology; Mathematics education; Psychology; Biology; Computer graphics (images); Physics; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.003718647,0.000669929,0.0003287118,0.0009502653,0.0004968466,0.002150085,0.001567234,0.0007849187,0.004946523],"category_scores_gemma":[0.01205898,0.000292929,0.0005396061,0.0003778178,0.00121567,0.002799297,0.002263082,0.001124113,0.0007736259],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006058583,"about_ca_system_score_gemma":0.0006522058,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008089565,"about_ca_topic_score_gemma":0.001070865,"domain_scores_codex":[0.9991998,0.0004950628,0.00004357909,0.00008934364,0.0001249781,0.00004722469],"domain_scores_gemma":[0.9966897,0.002194299,0.0001964056,0.0003503442,0.0003081688,0.0002610561],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00096536,0.0006829461,0.00846229,0.001376798,0.00008768214,0.0005313522,0.01583265,0.0248541,0.154491,0.1003214,0.02073231,0.6716622],"study_design_scores_gemma":[0.000531286,0.002355965,0.01273938,0.001317265,0.0002477127,0.001763879,0.005906174,0.3685311,0.1107936,0.1371701,0.3582279,0.000415662],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1303602,0.001061859,0.8501763,0.002851035,0.0002113165,0.0005276028,0.0003537431,0.004646962,0.00981101],"genre_scores_gemma":[0.2771489,0.001055628,0.7149743,0.0004517598,0.00007561318,0.0008386743,0.000374629,0.0008326391,0.00424781],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004946523,"threshold_uncertainty_score":0.01966631,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01736244559690015,"score_gpt":0.3781714061031645,"score_spread":0.3608089605062644,"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."}}