{"id":"W2123168697","doi":"10.1109/ccece.2008.4564821","title":"PCI-SS: Web-based human and machine interfaces for protein secondary structure prediction","year":2008,"lang":"en","type":"article","venue":"Conference proceedings - Canadian Conference on Electrical and Computer Engineering","topic":"Protein Structure and Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; SOAP; XML; Interface (matter); Web service; Conventional PCI; User interface; Distributed computing; World Wide Web; Operating system","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"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":[0.001695463,0.001689624,0.0007928677,0.001436756,0.000440618,0.00146402,0.001724836,0.0009848103,0.02455914],"category_scores_gemma":[0.005366515,0.0005331993,0.0008855593,0.001440658,0.000441945,0.001837558,0.001910693,0.001061592,0.01466897],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005323825,"about_ca_system_score_gemma":0.00119739,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00184461,"about_ca_topic_score_gemma":0.001414718,"domain_scores_codex":[0.9991003,0.0001517884,0.00008330083,0.0001594306,0.0004356384,0.00006958054],"domain_scores_gemma":[0.9983707,0.0007050985,0.0001469727,0.0002985696,0.0003081169,0.0001705179],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002402279,0.0005045111,0.006199765,0.0009681062,0.0002746598,0.001101816,0.0005913883,0.01399321,0.05522383,0.02361555,0.4941556,0.4009694],"study_design_scores_gemma":[0.0006449147,0.0003918419,0.005886405,0.000163211,0.000113928,0.001139386,0.0001571225,0.5491272,0.09281058,0.04478111,0.3045465,0.000237703],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007667504,0.0002254512,0.5347806,0.0002037146,0.0001305857,0.0003414456,0.009208065,0.4396864,0.007756232],"genre_scores_gemma":[0.1558199,0.0007803075,0.6945958,0.0009807132,0.0002569459,0.0015352,0.08078939,0.03289204,0.03234966],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02455914,"threshold_uncertainty_score":0.08215851,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008898767732175492,"score_gpt":0.1889853500973572,"score_spread":0.1800865823651817,"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."}}