{"id":"W4225930644","doi":"10.1007/978-3-030-98438-0_13","title":"Player Matching in a Persuasive Mobile Exergame: Towards Performance-Driven Collaboration and Adaptivity","year":2022,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Innovative Human-Technology Interaction","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Matching (statistics); Context (archaeology); Computer science; Persuasive technology; Baseline (sea); Dimension (graph theory); Group (periodic table); Intervention (counseling); Propensity score matching; Human–computer interaction; Psychology; Social psychology; Mathematics","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":[0.0006582576,0.0006273465,0.0004143072,0.0003143735,0.0003463814,0.001333461,0.001078971,0.0009958365,0.004985346],"category_scores_gemma":[0.001370525,0.0003115317,0.0003214788,0.0002732982,0.0003407091,0.001445259,0.00213739,0.0007145668,0.001878179],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001187354,"about_ca_system_score_gemma":0.0002819136,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002180757,"about_ca_topic_score_gemma":0.0002714531,"domain_scores_codex":[0.9994803,0.000145236,0.00002133462,0.0001593694,0.0001414977,0.00005229018],"domain_scores_gemma":[0.9996213,0.0002046591,0.00002940557,0.00004975693,0.00004526283,0.00004955794],"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":[0.001667373,0.001910098,0.00289567,0.0008771334,0.0001862644,0.0004385828,0.004017621,0.02243173,0.32284,0.03658038,0.00476483,0.6013902],"study_design_scores_gemma":[0.0003743992,0.004149177,0.01963479,0.0003112919,0.0004206047,0.002229961,0.002946935,0.6145593,0.1972525,0.08593594,0.07200072,0.0001843308],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.2645112,0.0005996632,0.6914303,0.0003474663,0.0001538065,0.0003589164,0.000101652,0.001796312,0.04070054],"genre_scores_gemma":[0.7738127,0.0003301784,0.1985859,0.000114525,0.0000463376,0.0003032214,0.0001933752,0.000231088,0.02638278],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.004985346,"threshold_uncertainty_score":0.01667762,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01459945924694972,"score_gpt":0.2606424470563652,"score_spread":0.2460429878094155,"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."}}