{"id":"W1493092051","doi":"","title":"Airborne remote sensing of forest leaf area index in mountainous terrain","year":2000,"lang":"en","type":"dissertation","venue":"Open ULeth Scholarship (OPUS) (University of Lethbridge)","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canadian Forest Service; Natural Resources Canada; Natural Sciences and Engineering Research Council of Canada; U.S. Forest Service; National Aeronautics and Space Administration; University of Lethbridge; University of Regina","keywords":"Terrain; Remote sensing; Leaf area index; Multispectral image; Normalized Difference Vegetation Index; Normalization (sociology); Scale (ratio); Environmental science; Pixel; Geography; Cartography; Computer science; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001086451,0.00007458958,0.00005511949,0.0001894091,0.000108492,0.000179829,0.00007567818,0.00006174079,0.0006951318],"category_scores_gemma":[0.0002102665,0.00005806856,0.00004266439,0.0004525384,0.00007330816,0.00008996989,0.00006021979,0.00008555494,0.0002199592],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002994898,"about_ca_system_score_gemma":0.0002069733,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.08701427,"about_ca_topic_score_gemma":0.1843836,"domain_scores_codex":[0.9999717,0.000002933205,6.526111e-7,0.000005492364,0.00001543769,0.000003782901],"domain_scores_gemma":[0.9999765,0.000005977429,0.000003216627,0.000001747562,0.00001007316,0.000002464931],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0001566027,0.0001442774,0.1831613,0.0002354062,0.00005053671,0.0002150954,0.001527653,0.01982539,0.09748047,0.001396807,0.009250774,0.6865558],"study_design_scores_gemma":[0.00001045554,0.00006526741,0.9695805,0.00002202892,0.00002189776,0.00008438775,0.0006039405,0.01210548,0.005010993,0.0004090846,0.0120781,0.000007833632],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9833136,0.001997513,0.004881299,0.0001519825,0.00001300139,0.00002424618,0.000524333,0.00008490315,0.009009182],"genre_scores_gemma":[0.9782645,0.002052035,0.01024364,0.00002966605,0.00001244496,0.00001020799,0.0007859697,0.00001372493,0.008587738],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08701427,"threshold_uncertainty_score":0.1730155,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01501118885990946,"score_gpt":0.2346680119048723,"score_spread":0.2196568230449629,"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."}}