{"id":"W22543326","doi":"10.12927/hcpap.2013.22860","title":"DERIVING THE OPTIMAL MODALITY COMBINATION FOR SEARCHING IN MULTIDIMENSIONAL DATABASES","year":2003,"lang":"en","type":"article","venue":"ICWI","topic":"Speech and dialogue systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Modality (human–computer interaction); Modalities; Set (abstract data type); Haptic technology; Human–computer interaction; Database; Artificial intelligence; Information retrieval","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000909019,0.00005264647,0.00006498482,0.00003958986,0.0001193941,0.00004162038,0.0001859477,0.00001466099,0.000002206513],"category_scores_gemma":[0.0003301363,0.00003882797,0.00002581212,0.0001262001,0.00001818296,0.0002663621,0.00006571141,0.00006096286,0.000007791502],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002658154,"about_ca_system_score_gemma":0.00006026125,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001889542,"about_ca_topic_score_gemma":0.00007369526,"domain_scores_codex":[0.9992474,0.0001361594,0.0001161688,0.0001723568,0.0001539043,0.0001740324],"domain_scores_gemma":[0.9992645,0.0003911864,0.00003233655,0.0002459395,0.00003456293,0.00003141993],"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":[0.0000143331,0.0001688953,0.01074915,0.00002845475,0.000009625646,0.000008554891,0.001286233,0.004810254,0.005249884,0.9695351,0.0006210998,0.007518443],"study_design_scores_gemma":[0.002407869,0.0001062686,0.06563427,0.0000913658,0.000004333591,0.0000311025,0.0003281596,0.8817244,0.02696435,0.01120103,0.01113062,0.0003763056],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3462226,0.00005313212,0.652374,0.0004393935,0.0002707685,0.0002372843,0.000004791642,0.00002959171,0.0003684473],"genre_scores_gemma":[0.9259372,7.270873e-7,0.07388753,0.00009296135,0.00002080848,0.00002585196,0.000006306606,0.000002968176,0.00002560516],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.958334,"threshold_uncertainty_score":0.1583359,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04985782978830274,"score_gpt":0.304777095552146,"score_spread":0.2549192657638433,"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."}}