{"id":"W2064232423","doi":"10.3758/bf03192703","title":"Generating complex three-dimensional stimuli (Greebles) for haptic expertise training","year":2005,"lang":"en","type":"article","venue":"Behavior Research Methods","topic":"Tactile and Sensory Interactions","field":"Neuroscience","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Canadian Institutes of Health Research; James S. McDonnell Foundation; National Science Foundation","keywords":"Haptic technology; Computer science; Set (abstract data type); Perception; Object (grammar); Training (meteorology); Artificial intelligence; Human–computer interaction; Psychology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001329136,0.0001951867,0.0002658595,0.0003074069,0.00113119,0.0001866113,0.0003810754,0.0001010437,0.0009302939],"category_scores_gemma":[0.003285314,0.0001826284,0.0001687095,0.0003631703,0.0002586859,0.0003577943,0.0001377588,0.0006226477,0.0001016248],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001776043,"about_ca_system_score_gemma":0.0001500899,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007451027,"about_ca_topic_score_gemma":0.0001184001,"domain_scores_codex":[0.9961605,0.001176101,0.0004117071,0.0006680916,0.0007139068,0.000869687],"domain_scores_gemma":[0.9953163,0.00364861,0.0000740564,0.0004423269,0.0002373567,0.0002813804],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00004082798,0.0001396125,0.000030696,0.00000426398,0.000002171897,0.00001731286,0.000487769,0.0004418832,0.6864049,0.0003082066,0.0005778402,0.3115445],"study_design_scores_gemma":[0.0008539064,0.000305525,0.0005791667,0.00003417587,0.00002134409,0.0001637248,0.000467916,0.4169594,0.546354,0.0002600553,0.03365547,0.0003453234],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8924058,0.00004073044,0.1024532,0.001389838,0.0004737717,0.001535328,0.00006980357,0.0002078714,0.001423669],"genre_scores_gemma":[0.6135893,0.000003737695,0.3835416,0.0003411126,0.0004322569,0.0006528475,0.000007324057,0.00004821844,0.00138362],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4165175,"threshold_uncertainty_score":0.999983,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.7364569336206933,"score_gpt":0.6112126661609603,"score_spread":0.125244267459733,"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."}}