{"id":"W1972330479","doi":"10.5539/ass.v10n13p108","title":"Interpretation of TRIZ Principles in a Service Related Context","year":2014,"lang":"en","type":"article","venue":"Asian Social Science","topic":"Software Engineering Techniques and Practices","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"TRIZ; Context (archaeology); Service (business); Usability; Interpretation (philosophy); Computer science; Process (computing); Contradiction; Management science; Knowledge management; Process management; Engineering; Artificial intelligence; Human–computer interaction; Epistemology; Marketing; Business","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001092986,0.00005511798,0.00009843805,0.0001197911,0.00007294662,0.00006136126,0.0007979846,0.00004118347,0.000003229107],"category_scores_gemma":[0.0003780066,0.00005390621,0.00002068281,0.001402298,0.0000902143,0.0007580596,0.0001378324,0.00009617129,0.000005926394],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000419109,"about_ca_system_score_gemma":0.00006915539,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008936393,"about_ca_topic_score_gemma":0.00002904915,"domain_scores_codex":[0.9991913,0.00005219312,0.0001808311,0.000196135,0.0002270919,0.0001524856],"domain_scores_gemma":[0.9994872,0.0001113413,0.0001177313,0.0001690616,0.0000816899,0.00003298662],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.000004069916,0.0000231754,0.0006541297,0.00001228104,0.000001711197,9.126163e-7,0.01015845,0.00001871836,0.001045106,0.361232,0.00001404655,0.6268354],"study_design_scores_gemma":[0.001071055,0.0004070743,0.4687271,0.0002813328,0.00001202258,0.00002121484,0.001182591,0.4238718,0.01333569,0.07032124,0.01995661,0.0008122519],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05628414,0.00003049749,0.8331286,0.003472048,0.0003577915,0.0002033202,7.070564e-7,0.0003735048,0.1061494],"genre_scores_gemma":[0.9837452,0.000001413775,0.01607461,0.0001418064,0.00001141334,0.000005162302,1.991676e-7,0.000002536676,0.00001770076],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.927461,"threshold_uncertainty_score":0.2198232,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01456683838198268,"score_gpt":0.2726259774556862,"score_spread":0.2580591390737035,"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."}}