{"id":"W2965464462","doi":"10.3390/rs11151760","title":"Field-Scale Crop Seeding Date Estimation from MODIS Data and Growing Degree Days in Manitoba, Canada","year":2019,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Agriculture and Agri-Food Canada","funders":"","keywords":"Seeding; Scale (ratio); Estimation; Environmental science; Degree (music); Remote sensing; Crop; Growing degree-day; Geography; Forestry; Agronomy; Cartography; Sowing; Biology; Physics","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0004674753,0.000447353,0.0002699387,0.001449596,0.001255465,0.0008391493,0.00081427,0.0001958516,0.0008920263],"category_scores_gemma":[0.001198156,0.0002618381,0.0002921693,0.002855004,0.0003159447,0.0002776246,0.0003518882,0.0002743555,0.0002081437],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02058146,"about_ca_system_score_gemma":0.02448545,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9967569,"about_ca_topic_score_gemma":0.9986191,"domain_scores_codex":[0.9997378,0.00001851598,0.00001050096,0.00007466652,0.00009326696,0.00006524796],"domain_scores_gemma":[0.9990708,0.00006326837,0.00005857023,0.00002544707,0.000707545,0.00007431836],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001844127,0.00009705012,0.8820284,0.000193944,0.000154711,0.0003402721,0.001186037,0.01891743,0.0131044,0.0007185964,0.007602645,0.07547205],"study_design_scores_gemma":[0.00002592726,0.0000180557,0.9503946,0.00008681395,0.00005567682,0.0000561626,0.00159733,0.03785647,0.002343648,0.0001268139,0.007381808,0.00005670877],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9755187,0.001224513,0.004866857,0.0002667401,0.00002246163,0.0001395953,0.009501403,0.0002805658,0.008179273],"genre_scores_gemma":[0.978322,0.0006709728,0.009717459,0.0000853388,0.000005603515,0.00006222934,0.007436125,0.00004045727,0.00365993],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02058146,"threshold_uncertainty_score":0.1493296,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02106136092900644,"score_gpt":0.2123313536856027,"score_spread":0.1912699927565963,"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."}}