{"id":"W2070742146","doi":"10.1109/ofs.1992.763116","title":"Fiber Optic Sensing for Smart Materials and Structures","year":2005,"lang":"en","type":"article","venue":"","topic":"Advanced Fiber Optic Sensors","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Occupational Cancer Research Centre; Institute for Christian Studies; University of Toronto","funders":"","keywords":"Multidisciplinary approach; Smart material; Field (mathematics); Aerospace engineering; Systems engineering; Computer science; Engineering; Remote sensing; Construction engineering; Geology; Nanotechnology; Materials science","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":[],"consensus_categories":[],"category_scores_codex":[0.00003074191,0.00009572198,0.0001101357,0.00002794053,0.00002651698,0.00002726322,0.00002352428,0.00004458208,0.0001997804],"category_scores_gemma":[0.00001524278,0.00008711491,0.00001316374,0.00002235018,0.00001598536,0.00007973794,0.000009638838,0.00002758042,0.00002319108],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001937104,"about_ca_system_score_gemma":0.000001669573,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001896384,"about_ca_topic_score_gemma":0.000003466286,"domain_scores_codex":[0.9995828,0.000002946127,0.0001137695,0.00009817328,0.0000384403,0.000163921],"domain_scores_gemma":[0.9997874,0.0000553562,0.000008845407,0.00009757229,0.00001300469,0.00003778658],"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":[0.00005242716,0.000009119643,0.00002344662,0.00034667,0.0001838131,0.00001146015,0.0007907453,0.1831098,0.6120818,0.01290253,0.005667633,0.1848206],"study_design_scores_gemma":[0.0009871022,0.00003723969,0.0006830078,0.00002767385,0.0000468273,0.0001544905,0.0001158389,0.09255207,0.831396,0.003312066,0.07004752,0.0006401238],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9859427,0.00009075828,0.009599425,0.0000662388,0.0001761394,0.0001774815,0.000008060538,0.0002753004,0.00366394],"genre_scores_gemma":[0.5629469,0.000006200676,0.4354256,0.00005143725,0.0001563218,0.000002192111,0.000003824544,0.00003488788,0.001372629],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4258261,"threshold_uncertainty_score":0.3552443,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009891256666760101,"score_gpt":0.2273902981879631,"score_spread":0.217499041521203,"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."}}