{"id":"W2009232824","doi":"10.1364/fts.2013.fw1d.4","title":"Imaging FTS for Hyperspectral Polarization Sensing in the LWIR: Application to Liquid Detection","year":2013,"lang":"en","type":"article","venue":"Imaging and Applied Optics","topic":"Analytical Chemistry and Sensors","field":"Chemical Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Defence Research and Development Canada","funders":"","keywords":"Hyperspectral imaging; Polarization (electrochemistry); Remote sensing; Optics; Materials science; Environmental science; Physics; Geology; Chemistry","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":[0.0001056851,0.0001093499,0.00009729632,0.00003818535,0.00009060918,0.00007433927,0.00006815936,0.00003407034,0.000001781477],"category_scores_gemma":[0.00005664775,0.00009478213,0.000025595,0.0001509466,0.00002745714,0.00006397188,0.00001789706,0.0001392409,0.00000894474],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003686089,"about_ca_system_score_gemma":0.000004018399,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003164956,"about_ca_topic_score_gemma":0.000001527455,"domain_scores_codex":[0.9993275,0.000005015801,0.0001562929,0.0002033279,0.00008744061,0.0002204047],"domain_scores_gemma":[0.9996537,0.00009623118,0.00002692577,0.0001278679,0.00003856311,0.00005672987],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001572611,0.0000134707,0.0000408325,0.0000238739,0.000003488369,3.704896e-7,0.000299964,0.002519559,0.985832,0.001766528,0.00001279001,0.009471397],"study_design_scores_gemma":[0.0002293186,0.000007128566,0.0001041946,0.00001216219,0.00001979505,0.00001124053,0.0009910373,0.7719139,0.225559,0.0007538131,0.0002319833,0.0001664698],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4939689,0.00002600258,0.5026935,0.001462917,0.00001997484,0.0003355347,0.000001669833,0.00007636169,0.00141513],"genre_scores_gemma":[0.9915555,0.000002004319,0.007849752,0.0003686711,0.0001403356,0.00002853699,0.000009441146,0.00001701665,0.0000287108],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7693943,"threshold_uncertainty_score":0.3865103,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005353289418928918,"score_gpt":0.2155807441538604,"score_spread":0.2102274547349315,"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."}}