{"id":"W4403341286","doi":"10.32604/cmc.2024.057094","title":"AI-Powered Innovations in High-Tech Research and Development: From Theory to Practice","year":2024,"lang":"en","type":"article","venue":"Computers, materials & continua/Computers, materials & continua (Print)","topic":"Big Data and Business Intelligence","field":"Business, Management and Accounting","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Development (topology); Engineering ethics; High tech; Engineering; Engineering management; Sociology; Management science; Political science; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.005833223,0.001051491,0.001532648,0.002074932,0.0005133456,0.009475375,0.001953847,0.0004948767,0.001953726],"category_scores_gemma":[0.0007927488,0.001042438,0.00008121942,0.002086536,0.0003843583,0.004200972,0.003888001,0.0006068513,0.003264501],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002451411,"about_ca_system_score_gemma":0.0002483833,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003276011,"about_ca_topic_score_gemma":0.000261869,"domain_scores_codex":[0.9923315,0.000453519,0.002354893,0.002278356,0.001002581,0.00157914],"domain_scores_gemma":[0.9953448,0.001304328,0.0005695777,0.001316464,0.001345262,0.0001196291],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.002170713,0.0009333296,0.0008297773,0.002152828,0.0007302811,0.001042,0.003192391,0.00002142608,0.5660344,0.1731626,0.1238481,0.1258821],"study_design_scores_gemma":[0.001716475,0.00009857173,0.01280249,0.00375346,0.0001191125,0.00005382465,0.0004442278,0.0001329878,0.08944534,0.01597471,0.8734221,0.002036637],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9022543,0.0005420651,0.06608171,0.006645607,0.02005594,0.00245882,0.0002977528,0.001093536,0.0005702099],"genre_scores_gemma":[0.9542794,0.00009952235,0.03150805,0.005539041,0.006089692,0.0003523967,0.001305657,0.0002651255,0.0005611075],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7495741,"threshold_uncertainty_score":0.9992026,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06015718674744946,"score_gpt":0.3286039589895827,"score_spread":0.2684467722421332,"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."}}