{"id":"W2405843357","doi":"","title":"Learning from a network of peers via peer-driven adjustment of a corpus.","year":2012,"lang":"en","type":"article","venue":"International Conference on User Modeling, Adaptation, and Personalization","topic":"Open Education and E-Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Similarity (geometry); Set (abstract data type); Probabilistic logic; Personalized learning; Order (exchange); Peer-to-peer; Value (mathematics); World Wide Web; Artificial intelligence; Multimedia; Machine learning; Mathematics education; Cooperative learning; Open learning; Teaching method","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":[0.003251961,0.0007110707,0.0009178355,0.0008509353,0.001300108,0.001666299,0.00281621,0.001110689,0.003927858],"category_scores_gemma":[0.01595461,0.0005417996,0.0006005976,0.0007789978,0.00104089,0.004257612,0.003545631,0.001294641,0.001361052],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007161617,"about_ca_system_score_gemma":0.001031765,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001888607,"about_ca_topic_score_gemma":0.002847428,"domain_scores_codex":[0.9980806,0.0008316793,0.00006565302,0.0005005996,0.0004333653,0.00008811642],"domain_scores_gemma":[0.9941369,0.003155042,0.0003140201,0.001273566,0.0006570626,0.0004634088],"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":[0.000688219,0.001383584,0.01253637,0.0005776855,0.0003707458,0.0009926301,0.006209651,0.4159936,0.04625382,0.05432831,0.009263638,0.4514017],"study_design_scores_gemma":[0.00007366191,0.0003207683,0.002379881,0.00003094503,0.00008314779,0.0002859338,0.0009142354,0.932178,0.00612231,0.04211685,0.01542322,0.00007120178],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1302832,0.0002687544,0.8571628,0.0004686888,0.00009985938,0.0007943445,0.0001116904,0.00169075,0.009119977],"genre_scores_gemma":[0.7161995,0.0002405277,0.2736916,0.0001366032,0.0001084066,0.0009705561,0.0003775677,0.0003062409,0.007969052],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003927858,"threshold_uncertainty_score":0.0171982,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0823489160565354,"score_gpt":0.2950400878113604,"score_spread":0.212691171754825,"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."}}