{"id":"W2382485842","doi":"","title":"A Subpixel Target Detection Approach Based on Endmember Extraction in Hyperspectral Image","year":2014,"lang":"en","type":"article","venue":"Science Technology and Engineering","topic":"Remote Sensing and Land Use","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Science North","funders":"","keywords":"Subpixel rendering; Endmember; Hyperspectral imaging; Principal component analysis; Pattern recognition (psychology); Artificial intelligence; Anomaly detection; Projection (relational algebra); Computer science; Subspace topology; Orthographic projection; Computer vision; Mathematics; Pixel; Algorithm","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":[0.0004236909,0.00008101276,0.00008181926,0.0008318779,0.0001208991,0.00003475668,0.00009122931,0.00008387031,0.0000113938],"category_scores_gemma":[0.0001424193,0.00006740363,0.00001181489,0.001012856,0.0001546863,0.000162568,0.000004052816,0.000219905,0.000009712112],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008079151,"about_ca_system_score_gemma":0.00001314073,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007741516,"about_ca_topic_score_gemma":0.00004344721,"domain_scores_codex":[0.9993047,0.000008187029,0.0000783182,0.0002418049,0.0001076236,0.0002593344],"domain_scores_gemma":[0.9997746,0.00002962832,0.00001656972,0.0001190374,0.00001414383,0.00004602032],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006202918,0.00006321613,0.06148377,0.00005025271,0.000004499692,0.00002100704,0.0001920185,0.5104094,0.1631599,0.001632385,0.00000828477,0.2629133],"study_design_scores_gemma":[0.0001303027,0.00006970474,0.08245414,0.000009078526,0.000001267803,0.00002873797,0.00006794628,0.908511,0.00819617,0.0001984821,0.0002418268,0.00009129851],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9739038,0.00002704853,0.01856416,0.0001614515,0.0001422763,0.00004963184,6.844942e-7,0.0001505307,0.00700044],"genre_scores_gemma":[0.9830186,0.000004148574,0.01691147,0.00002746043,0.00002433212,3.18165e-7,0.000001544153,0.000001964792,0.00001015558],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3981017,"threshold_uncertainty_score":0.274864,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00446936630558867,"score_gpt":0.1823999242316531,"score_spread":0.1779305579260644,"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."}}