{"id":"W2151978623","doi":"10.1142/s0129156408005552","title":"AIRIS — THE CANADIAN HYPERSPECTRAL IMAGER: CURRENT STATUS AND FUTURE DEVELOPMENTS","year":2008,"lang":"en","type":"article","venue":"International Journal of High Speed Electronics and Systems","topic":"Calibration and Measurement Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Defence Research and Development Canada","funders":"","keywords":"Hyperspectral imaging; Remote sensing; Spectrogram; Imaging spectrometer; Full spectral imaging; Pixel; Computer science; Data processing; Spectrometer; Software; Identification (biology); Computer vision; Artificial intelligence; Optics; Geography; Physics; Database","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001625322,0.0000833844,0.0001071017,0.0001311929,0.00008610626,0.0001039682,0.0001103035,0.00003342261,0.000007251657],"category_scores_gemma":[0.00000571709,0.00006003549,0.00002697292,0.00004549912,0.00002361589,0.0001233593,0.000007651879,0.0002096905,8.763893e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003258956,"about_ca_system_score_gemma":0.0002177426,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006071432,"about_ca_topic_score_gemma":0.001230098,"domain_scores_codex":[0.9992244,0.00001650765,0.0002316155,0.00005574318,0.0003022952,0.0001694369],"domain_scores_gemma":[0.9995838,0.000009174288,0.00006331992,0.00004037035,0.0001875603,0.0001158053],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004355729,0.0003257756,0.06382982,0.000303392,0.005088479,0.0008451359,0.01300978,0.005047982,0.08313594,0.3216915,0.2943246,0.211962],"study_design_scores_gemma":[0.000995334,0.0001054324,0.02176086,0.00007718908,0.00002744372,0.001835472,0.00018957,0.002495835,0.004939216,0.000536136,0.9667645,0.0002730236],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9573047,0.03647619,0.0003817167,0.00178632,0.002595709,0.0001557245,0.00001543409,0.0000418368,0.001242429],"genre_scores_gemma":[0.9838328,0.0153934,0.0001707398,0.00005067971,0.0004970925,0.000001226452,0.000004419408,0.000009535056,0.00004011409],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6724399,"threshold_uncertainty_score":0.2448176,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01285506808652714,"score_gpt":0.2273978740548603,"score_spread":0.2145428059683331,"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."}}