{"id":"W4413606830","doi":"10.26434/chemrxiv-2025-m6r2j","title":"Computer Vision for High-Throughput Materials Synthesis: A Tutorial for Experimentalists","year":2025,"lang":"en","type":"preprint","venue":"ChemRxiv","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Throughput; Computer science; Computer architecture; Operating system","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","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.002192734,0.000719286,0.001234368,0.0001597591,0.0004039354,0.001096169,0.001731728,0.0006149921,0.0008176183],"category_scores_gemma":[0.0007218149,0.0006713145,0.0002756464,0.000102338,0.0002694819,0.0002256327,0.001754527,0.0001878298,0.0000917887],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002825327,"about_ca_system_score_gemma":0.0003285277,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002162005,"about_ca_topic_score_gemma":0.000003884074,"domain_scores_codex":[0.9955283,0.000253635,0.001053581,0.001855469,0.0004885122,0.0008204647],"domain_scores_gemma":[0.9966766,0.0008830043,0.0007451524,0.001270436,0.0002849987,0.0001397574],"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":[0.0004834014,0.0001299757,0.000003766496,0.001711926,0.00002916415,0.000002459315,0.0002463759,0.0006368443,0.9779517,0.002767635,0.01576096,0.0002757763],"study_design_scores_gemma":[0.0007987403,0.0001718803,0.00004757771,0.0007860656,0.0001043821,0.000003277583,0.00001429066,0.001339304,0.9848918,0.005045827,0.006081096,0.0007157781],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8717058,0.0001457302,0.06955008,0.0008748121,0.05122576,0.004330643,0.001257112,0.0007030929,0.000206988],"genre_scores_gemma":[0.629208,0.00001572487,0.3529885,0.000493139,0.008925937,0.006650172,0.0005048534,0.0001338432,0.001079826],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2834384,"threshold_uncertainty_score":0.9999408,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01965207467980951,"score_gpt":0.323155692442289,"score_spread":0.3035036177624795,"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."}}