{"id":"W2591493368","doi":"10.5281/zenodo.15440131","title":"IMPROVING ACCURACY IN ROBOTIZED FIBER PLACEMENT","year":2013,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Robot Manipulation and Learning","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Robotiq (Canada)","funders":"","keywords":"Process (computing); Robotic arm; Task (project management); Robot manipulator; Fiber; Computer science; Robot; Work (physics); Position (finance); Mechanical engineering; Engineering; Simulation; Materials science; Composite material; Artificial intelligence; Systems engineering","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","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002175128,0.0003355345,0.0003548365,0.0002667164,0.0001218726,0.0004054544,0.0006748313,0.0002829602,0.001222739],"category_scores_gemma":[0.0009574892,0.0003854382,0.0001329755,0.0002531468,0.00004875501,0.0002363635,0.0006685942,0.0009635321,0.0003063904],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002237207,"about_ca_system_score_gemma":0.00008501395,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001140898,"about_ca_topic_score_gemma":0.0005718318,"domain_scores_codex":[0.9966846,0.001569889,0.0005860052,0.000494557,0.0002889591,0.0003760649],"domain_scores_gemma":[0.9970605,0.0008633189,0.0002454952,0.001227698,0.0004712924,0.0001317336],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000007849106,0.0002060085,0.001283987,0.0005676217,0.00008619547,0.00000609254,0.008717311,0.8802282,0.003968953,0.007366171,0.002129246,0.09543242],"study_design_scores_gemma":[0.0006933599,1.224072e-7,0.003931084,0.001188798,0.00001853825,0.000002617264,0.00009343029,0.9820623,0.004395486,0.00042252,0.006663525,0.0005281987],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2043597,0.002085787,0.6424731,0.003648741,0.0008722026,0.00174846,0.00000889247,0.001656531,0.1431466],"genre_scores_gemma":[0.9472963,0.0002440146,0.04082996,0.00004597978,0.00003369549,0.0001679781,0.0003208848,0.00009803546,0.01096319],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7429366,"threshold_uncertainty_score":0.9998598,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01607168940332103,"score_gpt":0.2246422681707612,"score_spread":0.2085705787674401,"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."}}