{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001117408,0.0005618609,0.00102535,0.0003301721,0.000301655,0.0001121582,0.001888419,0.0009257233,0.000628596],"category_scores_gemma":[0.0001308461,0.0006521372,0.0002790529,0.001042053,0.0003751309,0.001095075,0.000485482,0.001638117,0.0001684678],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007378975,"about_ca_system_score_gemma":0.0001471151,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.04714636,"about_ca_topic_score_gemma":0.06251729,"domain_scores_codex":[0.9963381,0.0004386743,0.0005670837,0.001050567,0.0009859141,0.0006196522],"domain_scores_gemma":[0.9977379,0.0001278312,0.0009004376,0.0009332179,0.00009136707,0.0002092454],"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":[0.008107554,0.0009833424,0.09368774,0.001202628,0.0005631386,0.002797584,0.03512884,0.0188395,0.06892587,0.0001079722,0.01552852,0.7541273],"study_design_scores_gemma":[0.001571272,0.0001548159,0.9808448,0.001512129,0.0001252227,0.00005866656,0.005150364,0.001924295,0.001773592,0.0006836689,0.005247504,0.0009536989],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9473366,0.00005865057,0.0001431636,0.0003979591,0.0002055565,0.0009532982,0.00003635226,0.00003664874,0.05083179],"genre_scores_gemma":[0.9765012,0.000123521,0.007679814,0.000159983,0.00003369891,5.668806e-8,0.0005991553,0.00006455437,0.01483802],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.887157,"threshold_uncertainty_score":0.999593,"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."}}