{"id":"W2106989228","doi":"10.1017/s0890060406060112","title":"Design space and typed feature logic","year":2006,"lang":"en","type":"article","venue":"Artificial intelligence for engineering design analysis and manufacturing","topic":"Speech and dialogue systems","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Context (archaeology); Grammar; Space (punctuation); Feature (linguistics); Programming language; Strengths and weaknesses; Natural language processing; Natural language; Artificial intelligence; Human–computer interaction; Linguistics; Epistemology; History; Philosophy","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":[0.002101798,0.0004178171,0.0003559313,0.001350703,0.001142816,0.003894654,0.00107104,0.001207105,0.00462382],"category_scores_gemma":[0.003300477,0.0003798288,0.0009334187,0.001407429,0.005754635,0.007068152,0.001847241,0.001793627,0.000689743],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002623155,"about_ca_system_score_gemma":0.001521763,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004225953,"about_ca_topic_score_gemma":0.002372985,"domain_scores_codex":[0.998178,0.0006283789,0.0001654351,0.0002863519,0.0005177784,0.0002241546],"domain_scores_gemma":[0.9982368,0.0009489358,0.0001352436,0.0002384736,0.0003359468,0.0001046017],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00001278095,0.000006218374,0.00009049998,0.00002812509,0.000004215929,0.00005481041,0.0001698217,0.001250857,0.0002062094,0.9870638,0.0008663594,0.01024625],"study_design_scores_gemma":[0.000009049802,0.00001072387,0.00003465428,0.00002055278,0.000005200241,0.00006968889,0.0000651187,0.00339778,0.0003413432,0.9788817,0.01715595,0.000008211447],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03074658,0.00624775,0.8561215,0.006891197,0.0003332726,0.0000550651,0.0002764948,0.0008794361,0.09844869],"genre_scores_gemma":[0.7375467,0.003501538,0.2186089,0.00178659,0.0003728414,0.000168043,0.0003576797,0.0002756156,0.03738203],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00462382,"threshold_uncertainty_score":0.01903242,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03017141613535088,"score_gpt":0.2355143149791464,"score_spread":0.2053428988437956,"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."}}