{"id":"W7033665984","doi":"","title":"Recruitment Algorithms for Vehicular Crowdsensing Networks","year":2019,"lang":"en","type":"dissertation","venue":"QSpace (Queen's University Library)","topic":"Youth, Politics, and Society","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Heuristic; Articular cartilage damage; Work (physics); Matching (statistics); Identification (biology); Term (time)","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001781433,0.0003465681,0.000482777,0.0001600917,0.001121453,0.0002252369,0.0005270278,0.0009509185,0.0001620323],"category_scores_gemma":[0.00005156146,0.0004306019,0.0005249959,0.0002575781,0.0001870668,0.0008555407,0.00007138865,0.0004830211,0.00003581682],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004257127,"about_ca_system_score_gemma":0.001037894,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01432252,"about_ca_topic_score_gemma":0.0007233308,"domain_scores_codex":[0.9977279,0.0002728963,0.0001887241,0.0005984987,0.0004304496,0.0007815672],"domain_scores_gemma":[0.9986075,0.000267452,0.0002819664,0.0003697321,0.0001582471,0.0003151666],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003339292,0.0002520298,0.002943086,0.0005364637,0.0007987882,0.0001386615,0.1227234,0.0007219286,0.000002362676,0.1271991,0.7399125,0.004437714],"study_design_scores_gemma":[0.0005660919,0.00007711816,0.0008596616,0.0001985941,0.0002375791,4.245279e-8,0.09403601,0.0001733453,0.00009154046,0.0005254967,0.9025369,0.0006976523],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.1535684,0.0008567375,0.007535127,0.1174257,0.0241024,0.01663687,0.0009606324,0.003245908,0.6756682],"genre_scores_gemma":[0.03158774,0.00162921,0.003461996,0.0004699084,0.001117838,0.000004436397,0.00180732,0.000102275,0.9598193],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.2841511,"threshold_uncertainty_score":0.9998146,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02789013869464636,"score_gpt":0.2658368562681047,"score_spread":0.2379467175734584,"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."}}