{"id":"W4409019383","doi":"10.1016/j.cirpj.2025.03.003","title":"Machine learning models for predicting volumetric errors based on scale and master balls artefact probing data","year":2025,"lang":"en","type":"article","venue":"CIRP journal of manufacturing science and technology","topic":"Advanced Measurement and Metrology Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dawson College; Université de Montréal; Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Scale (ratio); Computer science; Artificial intelligence; Machine learning; Cartography; Geography","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001466268,0.001155524,0.0006957756,0.001000849,0.0002540072,0.0007370415,0.001177866,0.001045474,0.000970417],"category_scores_gemma":[0.004315483,0.0003450541,0.0008987953,0.0007575917,0.0004451823,0.0007710782,0.0005294747,0.001031738,0.0004696303],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006368112,"about_ca_system_score_gemma":0.0005846858,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006081807,"about_ca_topic_score_gemma":0.004356676,"domain_scores_codex":[0.9995061,0.00009236006,0.00004531324,0.000157998,0.0001389472,0.00005931344],"domain_scores_gemma":[0.9979776,0.001150533,0.0002224811,0.0001402162,0.0004642589,0.00004500437],"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":[0.0001202721,0.0000789273,0.003417414,0.00005825687,0.0000263202,0.00003915353,0.00003023993,0.9186147,0.002424906,0.0003952661,0.0006578979,0.07413662],"study_design_scores_gemma":[0.000001519056,0.00001722829,0.0005166873,0.000004317906,0.000002710449,0.000006261963,0.000003920741,0.9983718,0.0007663721,0.0002104618,0.00009583416,0.000002976295],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2497568,0.0009959716,0.7436468,0.0002516111,0.0001159953,0.0001490455,0.0007168456,0.002551178,0.001815685],"genre_scores_gemma":[0.9037315,0.0002515457,0.09231322,0.00009643069,0.00003554595,0.0002008355,0.001393539,0.00007506972,0.001902218],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006081807,"threshold_uncertainty_score":0.01209283,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03013259894053781,"score_gpt":0.2505831989997685,"score_spread":0.2204506000592307,"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."}}