{"id":"W4414514211","doi":"10.1002/adfm.202513092","title":"Artificial Compound Eye for Clear Vision in Harsh Environment","year":2025,"lang":"en","type":"article","venue":"Advanced Functional Materials","topic":"Surface Roughness and Optical Measurements","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Alberta Innovates; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Engineering and Physical Sciences Research Council; Canada Foundation for Innovation","keywords":"Machine vision; Compound eye; Crawling; Tracking (education); Night vision; Motion (physics); Match moving; Pixel; Optical imaging","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":[0.0001465148,0.0001076036,0.0001627344,0.00005281985,0.00004814624,0.00002990193,0.00004632946,0.00005556334,0.0002815441],"category_scores_gemma":[0.00002650137,0.0001091805,0.00002768082,0.00005745516,0.0000176216,0.0001141727,0.00001741876,0.00003877371,0.00006886911],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001099165,"about_ca_system_score_gemma":0.000006623471,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002017041,"about_ca_topic_score_gemma":0.000002433778,"domain_scores_codex":[0.9992757,0.00001425645,0.0002585286,0.0001595213,0.0001138846,0.000178085],"domain_scores_gemma":[0.9997857,0.00005907065,0.00001668908,0.00009958098,0.00001437659,0.00002460586],"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":[0.0004154737,0.00007072827,0.0001563192,0.00008848318,0.00002648465,0.000001089044,0.000008086859,0.1445256,0.8420905,0.005464089,0.0001668157,0.006986392],"study_design_scores_gemma":[0.001575583,0.0001513577,0.03977209,0.0002012226,0.00003023484,4.871364e-7,0.00005363132,0.005001818,0.8853647,0.02234562,0.04508129,0.0004220081],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9722894,0.0001207431,0.02402123,0.0001095895,0.002375211,0.0003731984,0.00004533588,0.0000825625,0.0005827206],"genre_scores_gemma":[0.9968348,0.00002498706,0.002706402,0.00005787544,0.00006255649,0.0001273979,0.00003955033,0.00001660925,0.0001297975],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1395237,"threshold_uncertainty_score":0.4452253,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01657520747830408,"score_gpt":0.2486002557197663,"score_spread":0.2320250482414623,"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."}}