{"id":"W3003140699","doi":"10.1007/978-3-030-37070-1_66","title":"True Traceability Enabled by In-Line Laser Marking of Lead and Zinc Ingots","year":2020,"lang":"en","type":"book-chapter","venue":"˜The œminerals, metals & materials series","topic":"Additive Manufacturing Materials and Processes","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"Laserax (Canada)","funders":"","keywords":"Traceability; Manufacturing engineering; Engineering; Production line; Process (computing); Compensation (psychology); Product (mathematics); Computer science; Process engineering; Mechanical 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0006747453,0.0008056439,0.001684509,0.0001188516,0.00008778246,0.0002085266,0.0003526052,0.0003744572,0.001276022],"category_scores_gemma":[0.00006279341,0.0006339292,0.0001136088,0.00005607658,0.0003054785,0.0003229378,0.0002122508,0.0002637799,0.00002702048],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004484962,"about_ca_system_score_gemma":0.00003311859,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001009685,"about_ca_topic_score_gemma":0.0001324023,"domain_scores_codex":[0.9974667,0.0001269648,0.001190693,0.0005549092,0.0002568204,0.0004038588],"domain_scores_gemma":[0.9987901,0.0002007297,0.0003872625,0.0004590903,0.0000793618,0.00008343712],"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":[0.0004199247,0.0000310617,0.000006011535,0.004951447,0.0003817,0.00003174763,0.0004761999,0.0002465215,0.9870411,0.001575079,0.003893018,0.0009461608],"study_design_scores_gemma":[0.0005330412,0.0001473796,0.0000953656,0.0004548493,0.0002181859,0.00002322556,0.00004516837,0.00003307403,0.8191446,0.01095867,0.1674279,0.000918526],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9448447,0.009832745,0.0001133001,0.0005253109,0.002864879,0.001886843,0.003844946,0.0008124742,0.0352748],"genre_scores_gemma":[0.9607607,0.002230776,0.0003268969,0.0001063989,0.000536124,0.00009413676,0.000405297,0.0002429839,0.03529669],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1678965,"threshold_uncertainty_score":0.9996369,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01539723258318024,"score_gpt":0.2031389935643568,"score_spread":0.1877417609811765,"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."}}