{"id":"W2081936291","doi":"10.1186/s13673-014-0012-z","title":"Context-aware recommender for mobile learners","year":2014,"lang":"en","type":"article","venue":"Human-centric Computing and Information Sciences","topic":"Mobile Learning in Education","field":"Computer Science","cited_by":45,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lakehead University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Adaptation (eye); Ontology; Context (archaeology); Recommender system; Task (project management); Semantic Web; Mobile device; World Wide Web; Human–computer interaction; Context awareness; Multimedia; Mobile computing; Knowledge management; Engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001006596,0.0005068772,0.0009896198,0.001449306,0.0007640382,0.001116818,0.0012355,0.001153533,0.002408284],"category_scores_gemma":[0.00253991,0.0003111001,0.0007329258,0.001098597,0.0001381778,0.001498193,0.0008927793,0.0009617919,0.001535275],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005775595,"about_ca_system_score_gemma":0.0007343744,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0142199,"about_ca_topic_score_gemma":0.03781774,"domain_scores_codex":[0.9993408,0.0001375254,0.00006726913,0.0001916137,0.0002113997,0.00005121618],"domain_scores_gemma":[0.9987676,0.0003236117,0.0000766575,0.0002100367,0.0005428226,0.00007924172],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0006416961,0.0008044425,0.03769771,0.0008873908,0.0005569723,0.001024205,0.0009984617,0.04153705,0.03185822,0.01224149,0.02762112,0.8441311],"study_design_scores_gemma":[0.00008997967,0.0003231483,0.01246231,0.0001928485,0.0004227053,0.0008212161,0.000596829,0.9151388,0.01490191,0.006570475,0.04832378,0.0001560455],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1685523,0.004059521,0.8024558,0.001076506,0.0003864414,0.000549593,0.00156728,0.00779346,0.01355905],"genre_scores_gemma":[0.6290408,0.001293912,0.3599104,0.0002694559,0.0001079769,0.000231392,0.001517727,0.0001269324,0.007501476],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0142199,"threshold_uncertainty_score":0.0282743,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0287126487994775,"score_gpt":0.3103733507922832,"score_spread":0.2816607019928057,"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."}}