{"id":"W3179597376","doi":"10.1111/tgis.12787","title":"Building <i>k</i>‐partite association graphs for finding recommendation patterns from questionnaire data","year":2021,"lang":"en","type":"article","venue":"Transactions in GIS","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Cisco Systems","keywords":"Association rule learning; Recommender system; Computer science; Centrality; Data mining; Apriori algorithm; Graph; Association (psychology); Information retrieval; Theoretical computer science; Data science; Mathematics; Combinatorics; Psychology","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":[0.002902827,0.0009657242,0.001235712,0.009993491,0.001666539,0.002357547,0.001640099,0.001776836,0.00157515],"category_scores_gemma":[0.02113806,0.0009755497,0.002411205,0.007663728,0.001096327,0.002676975,0.001413389,0.001516505,0.001038065],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009417834,"about_ca_system_score_gemma":0.001144666,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01329019,"about_ca_topic_score_gemma":0.0192258,"domain_scores_codex":[0.9968318,0.001141402,0.0002580406,0.000933164,0.0005493788,0.0002863155],"domain_scores_gemma":[0.9762719,0.01634419,0.002198048,0.00257737,0.002102741,0.0005057222],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0005742469,0.0008157507,0.1713701,0.0009259376,0.001506046,0.001441364,0.002947317,0.387626,0.007726111,0.03714392,0.01230695,0.3756162],"study_design_scores_gemma":[0.00001435367,0.00008684675,0.007408245,0.00005934828,0.0001117676,0.0003286511,0.0005003704,0.9551451,0.0009377582,0.03252179,0.002847793,0.00003802204],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1136737,0.0005095479,0.8810487,0.0004923932,0.00004655684,0.000237134,0.001624285,0.0007914798,0.001576167],"genre_scores_gemma":[0.5918265,0.0005975474,0.399768,0.0002674222,0.0001075245,0.0004022911,0.004981787,0.0001332238,0.001915698],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01329019,"threshold_uncertainty_score":0.02642566,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04648396684555205,"score_gpt":0.3073326369621923,"score_spread":0.2608486701166402,"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."}}