{"id":"W3185635920","doi":"10.1145/3458305.3459594","title":"Enabling hyperspectral imaging in diverse illumination conditions for indoor applications","year":2021,"lang":"en","type":"article","venue":"","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Hyperspectral imaging; Computer science; Artificial intelligence; Computer vision; Noise (video); Remote sensing; Electromagnetic spectrum; Ground truth; Full spectral imaging; Identification (biology); Image (mathematics); Optics; Geography","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":[0.0004052872,0.000811131,0.0003840335,0.0006124644,0.0003415447,0.0007525334,0.0006698575,0.0006739374,0.00141625],"category_scores_gemma":[0.0006079322,0.000241755,0.000518933,0.0005372543,0.0004976672,0.00105476,0.0009166342,0.00102097,0.0008384758],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002977937,"about_ca_system_score_gemma":0.0003531636,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001349021,"about_ca_topic_score_gemma":0.004028152,"domain_scores_codex":[0.9996513,0.0000567371,0.000009492343,0.00009656286,0.0001393036,0.00004650073],"domain_scores_gemma":[0.999703,0.00006832259,0.00004703403,0.00007249226,0.00008462009,0.00002451628],"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":[0.0002666201,0.0003780336,0.003633823,0.0004365113,0.0001046324,0.0002629711,0.00020206,0.09403875,0.3752457,0.003079921,0.008766402,0.5135844],"study_design_scores_gemma":[0.00001966341,0.0000916697,0.004869426,0.00003849237,0.00004446783,0.0004078503,0.0001453549,0.8008114,0.1805848,0.003708197,0.009223906,0.00005475545],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08041538,0.0007880576,0.9092431,0.0004440658,0.0001058351,0.00007993489,0.0003679229,0.003141899,0.005413824],"genre_scores_gemma":[0.5117913,0.001074369,0.4813843,0.0004532796,0.0001218474,0.0001150395,0.001068011,0.0003638506,0.003627869],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00141625,"threshold_uncertainty_score":0.004737794,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0160879179955234,"score_gpt":0.2559863664634279,"score_spread":0.2398984484679045,"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."}}