{"id":"W2564349480","doi":"10.1109/iros.2016.7759042","title":"Development of a low-cost ultra-tiny line laser range sensor","year":2016,"lang":"en","type":"article","venue":"","topic":"Advanced Optical Sensing Technologies","field":"Physics and Astronomy","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Range (aeronautics); Computer science; Line (geometry); Laser; Image sensor; Calibration; Pixel; Electro-optical sensor; Artificial intelligence; Computer vision; Optics; Materials science; Engineering; Electronic engineering; Physics; Mathematics","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":[0.0002808938,0.0003029699,0.0003421262,0.0002641233,0.000188279,0.0003557708,0.001059728,0.0006581113,0.00131568],"category_scores_gemma":[0.0004937307,0.0003144086,0.0002398368,0.0002127922,0.0002377996,0.001068,0.0006263852,0.0005784235,0.000800627],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003199131,"about_ca_system_score_gemma":0.0003716208,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003487283,"about_ca_topic_score_gemma":0.0004086995,"domain_scores_codex":[0.9996054,0.0000226288,0.00001893464,0.00006815038,0.000270303,0.00001464365],"domain_scores_gemma":[0.9996792,0.00005493829,0.00005188324,0.00003080096,0.0001521218,0.00003121751],"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":[0.00004549883,0.0000651962,0.0006735566,0.0002100665,0.00001229556,0.0001518456,0.00006458147,0.005227312,0.9291471,0.002698931,0.001631126,0.06007262],"study_design_scores_gemma":[0.00002973433,0.0004649775,0.001079323,0.00002712712,0.00002285095,0.0009090626,0.00004237231,0.147537,0.8266622,0.0007212617,0.02243383,0.00007013817],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06177133,0.0008306439,0.9295682,0.0004357114,0.0002762993,0.0002373676,0.0002315663,0.001858399,0.004790433],"genre_scores_gemma":[0.2565664,0.0006028913,0.7370894,0.0003049385,0.00004493746,0.000249353,0.0002924931,0.0001143656,0.004735216],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00131568,"threshold_uncertainty_score":0.004401386,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01617503395899897,"score_gpt":0.2516220118909788,"score_spread":0.2354469779319798,"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."}}