{"id":"W2039819429","doi":"10.2747/1548-1603.47.2.260","title":"Multi-sensor Analyses of Vegetation Indices in a Semi-arid Environment","year":2010,"lang":"en","type":"article","venue":"GIScience & Remote Sensing","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Comparability; Remote sensing; Vegetation (pathology); Arid; Scale (ratio); Environmental science; Geography; Cartography; Mathematics; Ecology","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":[0.000762615,0.000267405,0.0002274039,0.001019872,0.0001616407,0.0003138668,0.0001501962,0.0001398817,0.0004900005],"category_scores_gemma":[0.001245372,0.0001158173,0.0003112609,0.000887557,0.00009755889,0.0004791362,0.0002360352,0.0001166087,0.00008029692],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001505061,"about_ca_system_score_gemma":0.00008359196,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001380083,"about_ca_topic_score_gemma":0.003563716,"domain_scores_codex":[0.9997012,0.0001202013,0.00002375214,0.0000471143,0.00008988145,0.00001789023],"domain_scores_gemma":[0.9994482,0.0002918645,0.00009960273,0.00003638832,0.0001033939,0.00002048857],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001703464,0.0004363929,0.4988907,0.000523323,0.001380536,0.0004135745,0.0008738989,0.07567134,0.2233516,0.001551951,0.0006631523,0.1945402],"study_design_scores_gemma":[0.00001849583,0.000448536,0.8100404,0.00002155069,0.0001852921,0.0002707842,0.0007197367,0.1565896,0.02916356,0.001218079,0.001275778,0.00004827376],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9862776,0.0001656563,0.01239839,0.00001396809,0.00001156175,0.00001677832,0.0002295796,0.00002807483,0.0008584464],"genre_scores_gemma":[0.9918143,0.00005203194,0.007760747,0.000005337086,0.000003223302,0.000009895989,0.0001647359,0.00000662136,0.0001831058],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001380083,"threshold_uncertainty_score":0.004033148,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02231385875835208,"score_gpt":0.2753647530987317,"score_spread":0.2530508943403796,"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."}}