{"id":"W1988323553","doi":"10.1115/power2013-98059","title":"A New Robotic Process for In Situ Heat Treatment on Large Steel Components","year":2013,"lang":"en","type":"article","venue":"","topic":"Aluminum Alloy Microstructure Properties","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hydro-Québec","funders":"","keywords":"Electromagnetic coil; Process (computing); Finite element method; Induction heating; Materials science; Welding; In situ; Mechanical engineering; Induction coil; Component (thermodynamics); Heating element; Volume (thermodynamics); Computer science; Engineering; Electrical engineering; Structural engineering; Physics","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":[0.0002214417,0.0004703169,0.0003253356,0.0003129438,0.0003334316,0.000269685,0.0007277362,0.0003496212,0.001997189],"category_scores_gemma":[0.0001916265,0.0002845456,0.0003090912,0.0001644507,0.0003098776,0.0005005868,0.000316474,0.0004995439,0.0005815468],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003060577,"about_ca_system_score_gemma":0.0003648166,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003418731,"about_ca_topic_score_gemma":0.001326869,"domain_scores_codex":[0.9997621,0.0000119825,0.00000725414,0.00004713083,0.00015503,0.00001640468],"domain_scores_gemma":[0.999859,0.00001922862,0.00004189999,0.00003848334,0.00002737677,0.00001386692],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00003076207,0.0000373252,0.0001504851,0.0001653337,0.000006947619,0.00005877815,0.00005675196,0.001652582,0.9720426,0.0009728228,0.0004974178,0.02432828],"study_design_scores_gemma":[0.00003298257,0.0008215088,0.002645374,0.00001366705,0.00003527688,0.0009767188,0.00003250664,0.02062402,0.9430057,0.0003145082,0.03145354,0.0000442283],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2278734,0.001606133,0.7550784,0.000201779,0.0002725188,0.0002563531,0.0001987373,0.002399449,0.01211319],"genre_scores_gemma":[0.5197777,0.0006430108,0.4652623,0.00006019931,0.00006272202,0.0001449034,0.00018315,0.0001744773,0.01369159],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001997189,"threshold_uncertainty_score":0.006681323,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02085601299557126,"score_gpt":0.2327425595669722,"score_spread":0.2118865465714009,"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."}}