{"id":"W2576646839","doi":"10.1007/s10791-016-9292-4","title":"Enhancing click models with mouse movement information","year":2017,"lang":"en","type":"article","venue":"Information Retrieval","topic":"Information Retrieval and Search Behavior","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Relevance (law); Computer science; Information retrieval; Search engine; Process (computing); Movement (music); Artificial intelligence; World Wide Web; Human–computer interaction; Programming language","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":["scholarly_communication","insufficient_payload"],"consensus_categories":["scholarly_communication"],"category_scores_codex":[0.0007458297,0.000214725,0.0001920592,0.0003168007,0.00101654,0.002861231,0.001379327,0.0001177018,0.00003801582],"category_scores_gemma":[0.0001620892,0.0001730688,0.00007360365,0.0002505794,0.00008388788,0.04976904,0.0003818257,0.000259195,0.0007804157],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000150508,"about_ca_system_score_gemma":0.0002404059,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000732414,"about_ca_topic_score_gemma":0.000006542178,"domain_scores_codex":[0.9974905,0.00002383472,0.0007633201,0.0001202422,0.001173527,0.000428634],"domain_scores_gemma":[0.9971603,0.00002952202,0.0007533819,0.001108001,0.0007505952,0.0001981729],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008530401,0.0001461092,0.001358564,0.0003846477,0.00012487,0.00001042178,0.02686745,0.01035719,0.0009824913,0.7371527,0.002940977,0.2188216],"study_design_scores_gemma":[0.007303429,0.001382068,0.01478967,0.0001760845,0.00003768646,0.00006110244,0.001507163,0.7228205,0.1669962,0.004903662,0.07814456,0.001877862],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07459474,0.000003416257,0.8994113,0.0008834596,0.0003345208,0.0006300039,0.0000282648,0.0003213146,0.02379304],"genre_scores_gemma":[0.9702352,0.00002258956,0.0258885,0.003067777,0.00006222414,0.00003320225,0.0001163241,0.000008877815,0.0005652608],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8956405,"threshold_uncertainty_score":0.9999976,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01890283138193856,"score_gpt":0.2518378594100482,"score_spread":0.2329350280281096,"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."}}