{"id":"W3012709331","doi":"","title":"Tracking Crop Leaf Area Index and Chlorophyll Content Using RapidEye Data in Northern Ontario, Canada","year":2014,"lang":"en","type":"article","venue":"AGUFM","topic":"Leaf Properties and Growth Measurement","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Index (typography); Crop; Leaf area index; Geography; Environmental science; Remote sensing; Forestry; Agronomy; Biology; Computer science","routes":{"ca_aff":false,"ca_fund":false,"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":[],"consensus_categories":[],"category_scores_codex":[0.0002546631,0.0001084729,0.0001508265,0.000006866016,0.0001524095,0.00007799981,0.0002496283,0.00004028471,0.00009070748],"category_scores_gemma":[0.0000468225,0.00004060644,0.00001586005,0.00008162873,0.00003631407,0.0001238368,0.0001316025,0.0001122419,0.000001603182],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001333958,"about_ca_system_score_gemma":0.00006214433,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9888217,"about_ca_topic_score_gemma":0.9997624,"domain_scores_codex":[0.9990484,0.00004158478,0.0001790189,0.0002878443,0.0002069313,0.0002362767],"domain_scores_gemma":[0.9996777,0.00003787962,0.00005400456,0.0001118915,0.00004335379,0.00007513507],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003280351,0.00003114692,0.9057243,0.000007108529,0.00001047093,0.000008695383,0.0001550567,0.0001009203,0.02336833,0.000008921785,0.00006169209,0.07049058],"study_design_scores_gemma":[0.0003043646,0.000102518,0.9661964,0.00005106046,0.000008810744,0.000005502835,0.000297775,0.002419509,0.0006550691,0.00003059004,0.0297056,0.000222764],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9986367,0.0002117946,0.000004223698,0.0006715616,0.0001047258,0.0001082978,0.000007381921,0.000008429532,0.0002468771],"genre_scores_gemma":[0.9993336,0.00000449154,0.00001317424,0.0004073587,0.00008835258,0.00000195472,0.00002307432,9.491673e-7,0.0001270005],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07026782,"threshold_uncertainty_score":0.1655882,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1244136375029957,"score_gpt":0.216456750904108,"score_spread":0.09204311340111222,"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."}}