{"id":"W1666138844","doi":"10.1063/1.2060467","title":"Clean Fabrication and Cleanliness Monitoring in SNO","year":2005,"lang":"en","type":"article","venue":"AIP conference proceedings","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Laurentian University","funders":"","keywords":"Fabrication; Materials science; Computer science; Nanotechnology","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":[0.0009742507,0.0004374107,0.0006627066,0.001368996,0.001005329,0.001284624,0.0006841045,0.0008416942,0.003363876],"category_scores_gemma":[0.001888412,0.0003601838,0.000295967,0.0007828127,0.0005142228,0.001288954,0.001334011,0.0004789184,0.0009479549],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008839188,"about_ca_system_score_gemma":0.0008940788,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002295756,"about_ca_topic_score_gemma":0.006050486,"domain_scores_codex":[0.9980205,0.0001498369,0.00004764539,0.000275055,0.001394116,0.000112906],"domain_scores_gemma":[0.9987286,0.0002474131,0.000209785,0.0001860863,0.000543431,0.00008481661],"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":[0.001665544,0.0002144015,0.02554034,0.0005095095,0.00008489464,0.001080099,0.001089148,0.01446545,0.6813221,0.005793493,0.01315739,0.2550776],"study_design_scores_gemma":[0.0000792401,0.0009622949,0.04832657,0.00009301571,0.0001031768,0.0009257054,0.001085671,0.09372585,0.7873819,0.005532758,0.06163776,0.0001460536],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7931905,0.002748163,0.1258433,0.000905689,0.000356499,0.0001942511,0.001151049,0.002602482,0.07300804],"genre_scores_gemma":[0.9415814,0.0006263983,0.04517702,0.0001913874,0.00004111628,0.00006698265,0.0007496914,0.0003374594,0.01122862],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003363876,"threshold_uncertainty_score":0.0112533,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02895510479074609,"score_gpt":0.251050596913445,"score_spread":0.2220954921226989,"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."}}