{"id":"W2019087970","doi":"10.1016/j.forsciint.2014.08.020","title":"Detection of single graves by airborne hyperspectral imaging","year":2014,"lang":"en","type":"article","venue":"Forensic Science International","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":38,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University; National Research Council Canada","funders":"","keywords":"Hyperspectral imaging; Remote sensing; Range (aeronautics); Pixel; Spectral signature; Global Positioning System; Computer science; Geology; Environmental science; Artificial intelligence; Telecommunications; 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":[],"consensus_categories":[],"category_scores_codex":[0.0002223189,0.0000761121,0.00007065849,0.0001827648,0.00004850502,0.00006264028,0.0002266975,0.00001792004,0.00001152515],"category_scores_gemma":[0.0002145153,0.00007795219,0.00003117528,0.0002536318,0.0003603059,0.0003354685,0.00002051699,0.00006151364,0.00001702722],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001671335,"about_ca_system_score_gemma":0.00001198805,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001859519,"about_ca_topic_score_gemma":0.0000072681,"domain_scores_codex":[0.999136,0.000007116865,0.0001635215,0.0001705388,0.000366599,0.0001562206],"domain_scores_gemma":[0.9995497,0.0000324938,0.00004964705,0.0001487363,0.0001784058,0.00004097136],"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":[0.000002129914,0.000008045057,0.0001410384,0.000002249197,0.00000315819,1.763226e-7,0.00007156174,0.0008260619,0.9402658,0.000723198,0.0002196886,0.05773684],"study_design_scores_gemma":[0.00007817327,0.00001479738,0.005217625,0.00001126459,0.000002332616,0.00001183169,0.00002976088,0.3348852,0.6578913,0.0007958098,0.0009967065,0.00006529086],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8407963,0.00002436212,0.1447473,0.00030664,0.001781275,0.0000559713,0.000005005361,0.0001642427,0.01211885],"genre_scores_gemma":[0.9941162,0.000001695109,0.005691906,0.00002091271,0.0001030507,9.405703e-7,0.000005701526,0.000009285234,0.00005033594],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3340591,"threshold_uncertainty_score":0.3178798,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009079958388462787,"score_gpt":0.221160331973484,"score_spread":0.2120803735850212,"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."}}