{"id":"W1537988608","doi":"10.1007/978-3-642-23765-2","title":"Human-Computer Interaction – INTERACT 2011","year":2011,"lang":"en","type":"book","venue":"Lecture notes in computer science","topic":"Innovative Human-Technology Interaction","field":"Computer Science","cited_by":268,"is_retracted":false,"has_abstract":false,"ca_institutions":"Queen's University","funders":"","keywords":"Human interaction; Set (abstract data type); Computer science; Volume (thermodynamics); Human–computer interaction; World Wide Web; Physics; 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":["metaepi_narrow","open_science","research_integrity","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001185541,0.0009954287,0.0008859247,0.003481482,0.0005063088,0.0008040044,0.0065448,0.0008268614,0.0002771321],"category_scores_gemma":[0.00007612473,0.0009669055,0.0002549138,0.001356339,0.001081733,0.0030172,0.003054169,0.003190389,0.001031447],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001708285,"about_ca_system_score_gemma":0.0007892295,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007843164,"about_ca_topic_score_gemma":0.0001381017,"domain_scores_codex":[0.9938176,0.0001496313,0.001167879,0.002711884,0.0009899278,0.001163056],"domain_scores_gemma":[0.995095,0.0003531021,0.0009974322,0.002600193,0.0008000587,0.0001541786],"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":[0.00002377037,0.0003398927,0.0001295126,0.0001057374,0.00009831991,0.0003978715,0.003330754,0.002677974,0.001859712,0.03682567,0.006098791,0.948112],"study_design_scores_gemma":[0.001500597,0.002480655,0.001853251,0.002573017,0.0000548222,0.001573877,0.000002046658,0.476813,0.03644898,0.4402918,0.03172237,0.004685604],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0006511495,0.00008859906,0.9762257,0.000433768,0.01056839,0.0005860599,0.00000360808,0.0006972583,0.01074542],"genre_scores_gemma":[0.3251795,0.00002433713,0.6576929,0.005471638,0.0040111,0.0001243719,0.00005999189,0.0002046786,0.007231459],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9434264,"threshold_uncertainty_score":0.9997464,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02883960757926643,"score_gpt":0.2916125158846128,"score_spread":0.2627729083053464,"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."}}