{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000592615,0.0004699384,0.0004351537,0.0003650257,0.0003909876,0.0007556691,0.0008156558,0.0007346635,0.001676984],"category_scores_gemma":[0.002615383,0.0002764843,0.0001818794,0.0003354322,0.0005497563,0.0006888972,0.0008129796,0.0006149377,0.0005737509],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005185219,"about_ca_system_score_gemma":0.0004042625,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002079435,"about_ca_topic_score_gemma":0.002074335,"domain_scores_codex":[0.9990393,0.00009034229,0.00003172026,0.0001607844,0.0006096666,0.00006829078],"domain_scores_gemma":[0.9981835,0.0007089049,0.0002025786,0.0003872851,0.0004821999,0.00003558284],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004427244,0.00008080715,0.001619253,0.0001584874,0.00003756104,0.0001947988,0.000345539,0.3027902,0.3069569,0.00785486,0.0009915851,0.3785272],"study_design_scores_gemma":[0.00002737399,0.0002254788,0.001753681,0.0000163794,0.00001443711,0.0002025389,0.0000376997,0.7316207,0.2579328,0.002551936,0.005584578,0.00003238834],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.09146356,0.0003893192,0.9034038,0.0001137092,0.0001038012,0.00002285936,0.00003477468,0.001224512,0.003243724],"genre_scores_gemma":[0.7609563,0.000196032,0.2356701,0.00004327849,0.00004755407,0.0000194292,0.00005653771,0.0001591672,0.002851582],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002079435,"threshold_uncertainty_score":0.005610049,"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."}}