{"id":"W2000215959","doi":"10.3390/rs6020925","title":"Separating Crop Species in Northeastern Ontario Using Hyperspectral Data","year":2014,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":52,"is_retracted":false,"has_abstract":true,"ca_institutions":"Algoma University; Nipissing University","funders":"Natural Sciences and Engineering Research Council of Canada; Northern Ontario Heritage Fund Corporation","keywords":"Canola; Crop; Hordeum vulgare; Hyperspectral imaging; Sowing; Agronomy; Growing season; Biology; Environmental science; Mathematics; Geography; Remote sensing; Poaceae","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":[0.0002077937,0.0003284042,0.0001875888,0.0009571434,0.001475208,0.000666238,0.0003133412,0.0001675029,0.0007765804],"category_scores_gemma":[0.0006085905,0.0001755794,0.0001791012,0.001788175,0.0003752409,0.0002565339,0.0003143394,0.0001695945,0.0001695159],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01007002,"about_ca_system_score_gemma":0.008126707,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9850892,"about_ca_topic_score_gemma":0.9974074,"domain_scores_codex":[0.9997497,0.00001085208,0.000008348151,0.00005229057,0.0001053116,0.00007346951],"domain_scores_gemma":[0.9994169,0.00004446043,0.00007972088,0.00001786753,0.0003791363,0.00006190636],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004543739,0.00005995919,0.8746956,0.0001966718,0.0000896768,0.000524424,0.003418184,0.002632139,0.05440163,0.000378459,0.003095618,0.06005338],"study_design_scores_gemma":[0.00001076916,0.00001449793,0.9869534,0.00001921605,0.00002423888,0.00005871275,0.002102696,0.003011423,0.002278826,0.00004895334,0.005460375,0.00001691076],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9934826,0.0002010435,0.000614245,0.00007837642,0.000005321764,0.00003772625,0.001590787,0.00002582668,0.003964019],"genre_scores_gemma":[0.9904714,0.0002949422,0.002442882,0.00006440996,0.000003051259,0.00002343452,0.00283494,0.00001020984,0.003854854],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01491076,"threshold_uncertainty_score":0.07306349,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07075312131496561,"score_gpt":0.3053231358940096,"score_spread":0.2345700145790439,"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."}}