{"id":"W2285984635","doi":"10.5194/isprsarchives-xl-1-w4-415-2015","title":"WORKFLOW FOR BUILDING A HYPERSPECTRAL UAV: CHALLENGES AND OPPORTUNITIES","year":2015,"lang":"en","type":"article","venue":"˜The œinternational archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Hyperspectral imaging; Photochemical Reflectance Index; Payload (computing); Workflow; Remote sensing; Computer science; Process (computing); Fidelity; Environmental science; Systems engineering; Artificial intelligence; Leaf area index; Engineering; Ecology; Telecommunications; Geography","routes":{"ca_aff":true,"ca_fund":true,"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":[0.003424853,0.001204498,0.0007964214,0.001354294,0.001363505,0.003170774,0.001728533,0.001120647,0.01453003],"category_scores_gemma":[0.004047468,0.000725845,0.0007789402,0.0008969397,0.0005897647,0.00231946,0.00162081,0.001771761,0.0123732],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009844557,"about_ca_system_score_gemma":0.002065207,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004861621,"about_ca_topic_score_gemma":0.007401385,"domain_scores_codex":[0.9987804,0.0002467989,0.0000904004,0.0002516658,0.0004923432,0.0001384333],"domain_scores_gemma":[0.9969127,0.0005631006,0.0001966842,0.0009155161,0.001024566,0.0003873997],"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":[0.0005187912,0.0004899575,0.006763413,0.0009047171,0.0001025537,0.00160256,0.001221553,0.02865384,0.125117,0.0115064,0.04907834,0.7740409],"study_design_scores_gemma":[0.0001932043,0.0008387346,0.01878438,0.0008348181,0.0001095624,0.003046616,0.003343941,0.3724502,0.145525,0.02888366,0.4255817,0.0004080819],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01197552,0.000404495,0.9539835,0.001502477,0.0001876602,0.001164388,0.0008767461,0.01671367,0.01319148],"genre_scores_gemma":[0.05804836,0.0003583384,0.9327914,0.0003012968,0.00004901169,0.0004218241,0.001691995,0.001453788,0.004883898],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01453003,"threshold_uncertainty_score":0.04860783,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05644040431796753,"score_gpt":0.2696334889580825,"score_spread":0.213193084640115,"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."}}