{"id":"W1968134102","doi":"10.5589/m06-009","title":"Studying mixed grassland ecosystems I: suitable hyperspectral vegetation indices","year":2006,"lang":"en","type":"article","venue":"Canadian Journal of Remote Sensing","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":81,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Parks Canada","keywords":"Hyperspectral imaging; Leaf area index; Grassland; Vegetation (pathology); Environmental science; Remote sensing; Enhanced vegetation index; Soil science; Ecosystem; Spatial variability; Grassland ecosystem; Physical geography; Vegetation Index; Geography; Normalized Difference Vegetation Index; Mathematics; Ecology; Statistics; Biology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005119349,0.0001740389,0.000255966,0.0001686745,0.0003136309,0.0001801153,0.0001614253,0.0001163622,0.00002920826],"category_scores_gemma":[0.00008562375,0.0001461558,0.000103476,0.0003742896,0.00009553684,0.0003011808,0.00001345736,0.0003285285,0.00005496964],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007610798,"about_ca_system_score_gemma":0.0001364944,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.07457473,"about_ca_topic_score_gemma":0.456117,"domain_scores_codex":[0.9984237,0.0001188417,0.0004263564,0.0001925494,0.0003608131,0.0004777731],"domain_scores_gemma":[0.9990044,0.00005170363,0.000382822,0.0001460335,0.00006221675,0.0003528402],"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":[0.0000535865,0.00005504095,0.1030886,0.000122048,0.000268797,0.006507946,0.008076941,0.05541708,0.1966884,0.00008682337,0.05587025,0.5737644],"study_design_scores_gemma":[0.003252407,0.0005141631,0.7165781,0.001585405,0.0004058233,0.01649769,0.006371002,0.07325771,0.0319053,0.004634473,0.1428731,0.002124835],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.984751,0.0003458408,0.001988616,0.0003395042,0.0009071853,0.0001105668,0.000001190985,0.00001293132,0.01154312],"genre_scores_gemma":[0.960723,0.000004359253,0.03851138,0.00004600671,0.0004121479,2.790858e-9,0.000003179761,0.0000202863,0.000279637],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6134894,"threshold_uncertainty_score":0.9315878,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008978383560144448,"score_gpt":0.1913886052403484,"score_spread":0.1824102216802039,"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."}}