{"id":"W2041840486","doi":"10.2525/ecb.50.163","title":"Summer-season Differences in NDVI and iTVDI among Vegetation Cover Types in Lake Mashu, Hokkaido, Japan Using Landsat TM Data","year":2012,"lang":"en","type":"article","venue":"Environment Control in Biology","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Normalized Difference Vegetation Index; Vegetation cover; Vegetation (pathology); Grassland; Shrub; Physical geography; Vegetation Index; Environmental science; Enhanced vegetation index; Vegetation types; Transpiration; Growing season; Geography; Ecology; Leaf area index; Biology; Botany","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0001376479,0.0001923338,0.0001668676,0.0006418145,0.0003810078,0.0002886769,0.0001581565,0.0001298007,0.0004839685],"category_scores_gemma":[0.0002196966,0.0001616731,0.0001556065,0.000812247,0.0002051886,0.0003086075,0.0002696858,0.00008512055,0.0000601183],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007244566,"about_ca_system_score_gemma":0.0004305224,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.09064271,"about_ca_topic_score_gemma":0.2168594,"domain_scores_codex":[0.999915,0.00001059097,0.000008644035,0.00002254829,0.00001658708,0.00002662504],"domain_scores_gemma":[0.9999018,0.00001207614,0.00002937469,0.000004238962,0.00002101149,0.00003164831],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00009974913,0.00003937718,0.9860744,0.00002837446,0.00005504028,0.0001563095,0.0009359127,0.0003459487,0.006681746,0.00002934583,0.0001574944,0.0053963],"study_design_scores_gemma":[0.000001822778,0.000006878286,0.9992394,9.45111e-7,0.000008613533,0.00001020807,0.0002054022,0.0003785412,0.00009296913,0.000004059876,0.00004968587,0.000001491797],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9997827,0.00001574092,0.00002223798,0.000005308739,4.53336e-7,0.000001513468,0.00008066713,0.000001730667,0.00008967285],"genre_scores_gemma":[0.9994105,0.00002314183,0.000117615,0.000003223562,0.000001791368,0.000004945739,0.0002739054,0.000001156891,0.0001636383],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09064271,"threshold_uncertainty_score":0.1802302,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02705789340356254,"score_gpt":0.2473791057812923,"score_spread":0.2203212123777298,"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."}}