{"id":"W1965620289","doi":"10.1007/s40194-014-0116-0","title":"Predicting the hardness profile across resistance spot welds in martensitic steels","year":2014,"lang":"en","type":"article","venue":"Welding in the World","topic":"Advanced Welding Techniques Analysis","field":"Engineering","cited_by":29,"is_retracted":false,"has_abstract":false,"ca_institutions":"ArcelorMittal (Canada)","funders":"ArcelorMittal","keywords":"Martensite; Materials science; Welding; Solid mechanics; Metallurgy; Softening; Spot welding; Work (physics); Heat-affected zone; Hot work; Mechanical engineering; Tool steel; Composite material; Microstructure; Engineering","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":[0.0001538507,0.0003630046,0.0002504639,0.0007104392,0.000227328,0.0005839618,0.000374185,0.0004899277,0.0009061888],"category_scores_gemma":[0.0006132705,0.0002941756,0.0002773603,0.0004585613,0.0001581508,0.0004159476,0.0001618839,0.0002636548,0.0003362185],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003324407,"about_ca_system_score_gemma":0.0001665883,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007093822,"about_ca_topic_score_gemma":0.008733751,"domain_scores_codex":[0.9999357,0.000005796051,0.000001964645,0.00001832439,0.00002744845,0.00001084933],"domain_scores_gemma":[0.9998141,0.00005961007,0.00003450565,0.00002108019,0.00005631752,0.00001424922],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0008123557,0.0002047532,0.0897022,0.0001267395,0.00008960638,0.0003769069,0.0003021313,0.6350574,0.1876062,0.0009538755,0.000864688,0.0839031],"study_design_scores_gemma":[0.000008641091,0.0001340679,0.07382192,0.000002988438,0.0000161851,0.00006315964,0.0000743459,0.9020065,0.02321995,0.0003036524,0.000333482,0.00001514124],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.986059,0.00006434042,0.01219035,0.00001776265,0.000004435468,0.00000822013,0.000112333,0.0003078234,0.001235822],"genre_scores_gemma":[0.9978825,0.00002181822,0.001310881,0.00000192772,0.000001512271,0.000001464928,0.00009394087,0.00003130605,0.0006544964],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007093822,"threshold_uncertainty_score":0.01410508,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009353786170597305,"score_gpt":0.250858019967961,"score_spread":0.2415042337973637,"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."}}