{"id":"W4366773931","doi":"10.3390/machines11040496","title":"Deep Learning to Directly Predict Compensation Values of Thermally Induced Volumetric Errors","year":2023,"lang":"en","type":"article","venue":"Machines","topic":"Advanced Measurement and Metrology Techniques","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Compensation (psychology); Workspace; Moment (physics); Phase (matter); Sequence (biology); Computer science; Point (geometry); Power (physics); Quality (philosophy); Control theory (sociology); Artificial intelligence; Simulation; Algorithm; Mathematics; Physics; Geometry; Chemistry","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002695825,0.0001064341,0.0001637534,0.0004216279,0.00004365068,0.000005595793,0.0001061197,0.00005514786,0.00001924218],"category_scores_gemma":[0.0001954597,0.0001001823,0.00003841499,0.0007488727,0.00001166573,0.00006842328,0.00002850727,0.0001261323,0.00002277859],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002365921,"about_ca_system_score_gemma":0.000003530717,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001760194,"about_ca_topic_score_gemma":0.00001917917,"domain_scores_codex":[0.9993754,0.0000363436,0.0001650356,0.0001083011,0.0001486782,0.0001662094],"domain_scores_gemma":[0.9997216,0.00005744403,0.00003227789,0.0001067614,0.0000441188,0.00003780559],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00002607199,0.00001829983,0.1548779,0.00008200442,0.00007234611,0.000003210528,0.001061264,0.1314558,0.5572798,0.00005445631,0.0003602651,0.1547086],"study_design_scores_gemma":[0.0004035791,0.0003010746,0.6304212,0.00005514115,0.00004358293,0.000001661463,0.0001215515,0.2647412,0.1011757,0.0007075716,0.001692202,0.0003355288],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9792834,0.0001477442,0.01596079,0.00002913956,0.0002097035,0.0001607018,0.000001215783,0.001436051,0.002771289],"genre_scores_gemma":[0.9973515,0.00003670145,0.002318887,0.00001496862,0.00005000242,0.0000260781,0.0000133637,0.00002886685,0.0001596652],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4755433,"threshold_uncertainty_score":0.4085315,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02059268713417849,"score_gpt":0.2623452374533823,"score_spread":0.2417525503192038,"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."}}