{"id":"W2896673678","doi":"10.2351/1.5060988","title":"Adaptive metal deposition and data management for automated overhaul of complex turbine components","year":2007,"lang":"en","type":"article","venue":"","topic":"Manufacturing Process and Optimization","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lockheed Martin (Canada)","funders":"","keywords":"Turbine; Computer science; Deposition (geology); Engineering; Mechanical engineering; Geology","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":[0.001774736,0.0007635651,0.0007683277,0.001481562,0.0005585031,0.00219382,0.002166089,0.0008027588,0.003360278],"category_scores_gemma":[0.002811958,0.0003549358,0.0005107219,0.001409383,0.0005624656,0.002186095,0.002085607,0.0008615288,0.002196965],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000622218,"about_ca_system_score_gemma":0.0008829155,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001568465,"about_ca_topic_score_gemma":0.0009259012,"domain_scores_codex":[0.9983266,0.0002310185,0.0002147656,0.0004742475,0.0005929632,0.0001603847],"domain_scores_gemma":[0.9975344,0.0004548126,0.0001923212,0.001243385,0.0004015,0.0001737037],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001873112,0.000859144,0.01204265,0.0008694379,0.0002657252,0.001673411,0.001627753,0.02415247,0.1444615,0.01296357,0.04010135,0.7591098],"study_design_scores_gemma":[0.000299731,0.0005512675,0.01967039,0.0001920548,0.0002430273,0.001404975,0.0006295222,0.5130503,0.2709124,0.01502088,0.1777675,0.0002578998],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07613342,0.001185391,0.8089646,0.0005186546,0.0002443919,0.0006835376,0.002689408,0.1004622,0.00911842],"genre_scores_gemma":[0.6628271,0.0008756592,0.3113042,0.0005471526,0.0001986211,0.0007470826,0.008616486,0.002571221,0.01231239],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003360278,"threshold_uncertainty_score":0.01124126,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04066567728089649,"score_gpt":0.2674951805963232,"score_spread":0.2268295033154267,"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."}}