{"id":"W2114646553","doi":"10.1109/cibcb.2006.330959","title":"Optimization of the Sliding Window Size for Protein Structure Prediction","year":2006,"lang":"en","type":"article","venue":"","topic":"Protein Structure and Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Sliding window protocol; Window (computing); Protein structure prediction; Protein secondary structure; Sequence (biology); Probabilistic logic; Computer science; Algorithm; Protein structure; Biological system; Artificial intelligence; Chemistry; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002356572,0.0006475059,0.0009628847,0.0008653994,0.0003123885,0.0005864506,0.0008059838,0.0007607228,0.0009309478],"category_scores_gemma":[0.008232492,0.000425431,0.000506832,0.0007874003,0.0002828419,0.001600131,0.0004806441,0.0006359206,0.0003177298],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000346053,"about_ca_system_score_gemma":0.0007295496,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001825385,"about_ca_topic_score_gemma":0.001777736,"domain_scores_codex":[0.9991388,0.000331514,0.00007786614,0.0002063146,0.0001825684,0.00006280573],"domain_scores_gemma":[0.9964426,0.002733433,0.0001897833,0.0002189646,0.0003210683,0.000094101],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001678128,0.0002937017,0.007407912,0.0003484785,0.0002483685,0.0002587924,0.0002147727,0.3448805,0.1059112,0.005199559,0.002182925,0.5313757],"study_design_scores_gemma":[0.00004567127,0.0001796052,0.001963672,0.00001208428,0.00004305773,0.00007111984,0.00002301615,0.9781116,0.0169882,0.001657489,0.0008873011,0.00001712472],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.141809,0.001425049,0.8539622,0.0001189858,0.00006356585,0.0001030814,0.0001222986,0.001644665,0.0007511287],"genre_scores_gemma":[0.4972489,0.0006122452,0.5009555,0.00003713281,0.00003963234,0.0001864696,0.0002845708,0.0001794771,0.0004561466],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002356572,"threshold_uncertainty_score":0.01246291,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.002660111671391299,"score_gpt":0.1913130522731334,"score_spread":0.1886529406017421,"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."}}