{"id":"W165942106","doi":"10.1007/978-3-540-27780-4_28","title":"Utilizing Artificial Learners to Help Overcome the Cold-Start Problem in a Pedagogically-Oriented Paper Recommendation System","year":2004,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Intelligent Tutoring Systems and Adaptive Learning","field":"Computer Science","cited_by":39,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Computer science; Cold start (automotive); Artificial intelligence; Recommender system; Multimedia; Machine learning; Engineering","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":[0.002873566,0.0009946456,0.001069022,0.0006380393,0.0005661637,0.00215868,0.002126724,0.002587355,0.00316793],"category_scores_gemma":[0.0135003,0.000535489,0.0004265082,0.0006382716,0.0003866781,0.002545582,0.001011283,0.0017826,0.002233972],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003282136,"about_ca_system_score_gemma":0.0006875955,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001714709,"about_ca_topic_score_gemma":0.003614351,"domain_scores_codex":[0.9986412,0.0006017839,0.0001513258,0.0002622031,0.0002797472,0.00006370994],"domain_scores_gemma":[0.9903218,0.006577007,0.000420351,0.0007884753,0.001545294,0.0003470089],"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":[0.002218383,0.003897308,0.009869996,0.0006388229,0.000304066,0.0007409446,0.002481093,0.01852627,0.09426885,0.00226895,0.01050238,0.854283],"study_design_scores_gemma":[0.0007991179,0.003560057,0.004761512,0.0001105177,0.0007137801,0.001362815,0.000630025,0.7630538,0.1910094,0.005304697,0.02841715,0.000277269],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.4031574,0.0007706837,0.5680139,0.0008354093,0.0003814998,0.000916712,0.0002035579,0.01778662,0.00793425],"genre_scores_gemma":[0.5133744,0.0004166206,0.4665558,0.0006854829,0.0001641979,0.0004937727,0.0005403367,0.0005956703,0.01717369],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00316793,"threshold_uncertainty_score":0.0151971,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03891488039932822,"score_gpt":0.270496539395071,"score_spread":0.2315816589957427,"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."}}