{"id":"W2581737236","doi":"10.5061/dryad.79310","title":"Data from: What makes a multimodal signal attractive? A preference function approach","year":2017,"lang":"en","type":"article","venue":"Data Archiving and Networked Services (DANS)","topic":"Speech and dialogue systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Wilfrid Laurier University","funders":"","keywords":"Computer science; Preference; Function (biology); SIGNAL (programming language); Artificial intelligence; Mathematics; Statistics","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":[0.004030921,0.000741129,0.0009364184,0.001955368,0.0004977809,0.002661382,0.001235428,0.001501169,0.02695306],"category_scores_gemma":[0.0190569,0.0002602323,0.001388564,0.002141156,0.0007043909,0.002794669,0.001409568,0.001107646,0.005773695],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001228299,"about_ca_system_score_gemma":0.0005990064,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003003796,"about_ca_topic_score_gemma":0.003033196,"domain_scores_codex":[0.9977239,0.0008968678,0.0001892103,0.000479426,0.0005349037,0.0001757702],"domain_scores_gemma":[0.9928749,0.003921753,0.0005801506,0.0009856203,0.001394591,0.0002429095],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.003621127,0.0005662587,0.1443264,0.005185771,0.001259046,0.0009986165,0.001750862,0.02759549,0.05874837,0.1014528,0.03326254,0.6212327],"study_design_scores_gemma":[0.0004066594,0.003005908,0.2936313,0.001249583,0.001335846,0.004612813,0.00496357,0.28492,0.03998146,0.2064245,0.1589155,0.0005528327],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"dataset","genre_scores_codex":[0.5359474,0.005936374,0.3149085,0.01094935,0.0005006848,0.0009612277,0.04275436,0.002432621,0.08560947],"genre_scores_gemma":[0.9297181,0.001221357,0.04960404,0.001032631,0.000122308,0.0004636803,0.007026354,0.0002718385,0.01053957],"genre_candidate":"dataset","genre_consensus":null,"teacher_disagreement_score":0.02695306,"threshold_uncertainty_score":0.09016699,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1055848489951184,"score_gpt":0.2811252793829993,"score_spread":0.1755404303878809,"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."}}