{"id":"W3016868175","doi":"10.1177/1063293x20911165","title":"A model for supporting the ideas screening during front end of the innovation process based on combination of methods of EcaTRIZ, AHP, and SWOT","year":2020,"lang":"en","type":"article","venue":"Concurrent Engineering","topic":"Multi-Criteria Decision Making","field":"Decision Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"SWOT analysis; Analytic hierarchy process; Process (computing); Product (mathematics); Process management; Front and back ends; Product innovation; New product development; Engineering; Innovation management; Management science; Hierarchy; Computer science; Knowledge management; Engineering management; Operations research; Marketing; Business; Economics","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.002899498,0.0001369681,0.0003371687,0.0002465232,0.00007293212,0.00004232645,0.0004950031,0.0000469062,0.00001100347],"category_scores_gemma":[0.01097261,0.00008786271,0.00009614795,0.0008128464,0.00005546485,0.0001620947,0.0001341611,0.0001367681,1.415229e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001635661,"about_ca_system_score_gemma":0.00006052111,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001657687,"about_ca_topic_score_gemma":3.889849e-7,"domain_scores_codex":[0.9976617,0.00009139355,0.001132647,0.0002799476,0.0006809834,0.0001533918],"domain_scores_gemma":[0.9964854,0.001723627,0.0008662351,0.0002929411,0.0005959938,0.00003587982],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006936345,0.00003060247,0.001698609,0.0003380666,0.00001733323,1.174977e-7,0.002527445,0.814233,0.1062483,0.002775176,0.00001972729,0.07204228],"study_design_scores_gemma":[0.0006830742,0.00003696474,0.003227035,0.0001906597,0.00001587699,3.801335e-7,0.0002413548,0.9326786,0.06235297,0.00046524,0.00002998607,0.00007790387],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4052662,0.00006283101,0.5940349,0.0001816759,0.0001103191,0.000306942,0.00001775041,0.00001025455,0.000009122465],"genre_scores_gemma":[0.9749951,8.108116e-7,0.02490806,0.0000285616,0.00002349272,0.00002223465,0.000002514276,0.00001467894,0.000004578826],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5697289,"threshold_uncertainty_score":0.9973584,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2300205254457624,"score_gpt":0.4617194327767705,"score_spread":0.231698907331008,"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."}}