{"id":"W4379230434","doi":"10.3390/rs15112888","title":"Developing a New Vegetation Index Using Cyan, Orange, and Near Infrared Bands to Analyze Soybean Growth Dynamics","year":2023,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lakehead University","funders":"Agriculture and Agri-Food Canada; Lakehead University","keywords":"Cyan; Orange (colour); Leaf area index; Growing season; Environmental science; Remote sensing; Mathematics; Horticulture; Botany; Biology; Geography; Physics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.0007308836,0.0006661586,0.0004166831,0.001573991,0.0001954345,0.0008288817,0.0003301332,0.0003274413,0.0003782153],"category_scores_gemma":[0.0006425406,0.0002431202,0.0004927795,0.0008919499,0.0001175792,0.001119517,0.0003620831,0.0004252596,0.0001665222],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003327863,"about_ca_system_score_gemma":0.0003923515,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003735252,"about_ca_topic_score_gemma":0.01103172,"domain_scores_codex":[0.9997019,0.0000416686,0.00002745478,0.00007669578,0.0001277456,0.00002455165],"domain_scores_gemma":[0.9996715,0.00005457119,0.00006376168,0.00001867403,0.0001553784,0.00003619426],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003512988,0.0003769051,0.3170173,0.0004104355,0.0003772051,0.0001993091,0.0003113237,0.01306909,0.3023075,0.000889064,0.001424889,0.3632658],"study_design_scores_gemma":[0.00006776882,0.00111921,0.5327792,0.00008556331,0.0004551187,0.000690781,0.0005887125,0.3381411,0.1153738,0.0008100342,0.009655017,0.0002338135],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8328555,0.001377377,0.1601038,0.00009258366,0.00007717535,0.0001905621,0.001023304,0.000490537,0.003789134],"genre_scores_gemma":[0.7813292,0.000930377,0.2138974,0.00007043482,0.00004631835,0.0001717586,0.001523141,0.00006828906,0.001963211],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003735252,"threshold_uncertainty_score":0.007426977,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01595297249477059,"score_gpt":0.246717746852771,"score_spread":0.2307647743580004,"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."}}