{"id":"W4241911445","doi":"10.26434/chemrxiv.12736289.v1","title":"Image Analysis of Structured Surfaces for Quantitative Topographical Characterization","year":2020,"lang":"en","type":"preprint","venue":"ChemRxiv","topic":"Surface Roughness and Optical Measurements","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; McMaster University","funders":"Ontario Ministry of Research and Innovation; Natural Sciences and Engineering Research Council of Canada; McMaster University","keywords":"Microscale chemistry; Image stitching; Computer science; Feature (linguistics); Image processing; Feature extraction; Fourier transform; Metrology; Artificial intelligence; Materials science; Characterization (materials science); Digital image processing; Computer vision; Nanotechnology; Image (mathematics); Optics; Mathematics","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":[0.0003715322,0.0005540935,0.0003252178,0.001741731,0.0002200191,0.0008926028,0.0004030149,0.000529313,0.004103519],"category_scores_gemma":[0.001183694,0.0003162849,0.0003121161,0.001034706,0.0003292657,0.0006942966,0.0004019892,0.0007408854,0.001615843],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000325532,"about_ca_system_score_gemma":0.0003925723,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006804931,"about_ca_topic_score_gemma":0.0008924796,"domain_scores_codex":[0.9996588,0.00002807228,0.00001969474,0.00007336363,0.0001808648,0.00003922096],"domain_scores_gemma":[0.9993485,0.0002199339,0.00008126852,0.0001209043,0.000206502,0.0000230433],"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":[0.00007142191,0.00004597478,0.0005185102,0.0002330485,0.00002137914,0.0000975814,0.000115367,0.001865034,0.9270933,0.002266607,0.001679235,0.06599259],"study_design_scores_gemma":[0.00001759128,0.00008445321,0.008273831,0.00003206791,0.00002841607,0.0006036091,0.000129232,0.1073247,0.8699175,0.002503582,0.01103541,0.00004964715],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1364006,0.0007584123,0.8484567,0.0002708267,0.0001041165,0.0001822524,0.001878304,0.006024309,0.005924385],"genre_scores_gemma":[0.2536927,0.0008264969,0.7382994,0.000121042,0.00006249775,0.0002639834,0.002274201,0.001359743,0.003099918],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004103519,"threshold_uncertainty_score":0.01372761,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04149944054102291,"score_gpt":0.2792053382294295,"score_spread":0.2377058976884066,"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."}}