{"id":"W4241771564","doi":"10.1002/essoar.10507479.2","title":"Medium-resolution multispectral satellite imagery in precision agri- culture: mapping precision canola (Brassica napus L.) yield using Sentinel-2 time series","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Canola; Multispectral image; Brassica; Satellite; Computer science; Remote sensing; Artificial intelligence; Geography; Horticulture; Engineering; Agronomy; Biology","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":[0.0007198622,0.0004388838,0.000190581,0.0005225231,0.0002365432,0.0008323661,0.0004286867,0.000325299,0.001149209],"category_scores_gemma":[0.0009903059,0.0001518991,0.000245545,0.001279032,0.0002748661,0.0005531423,0.0003482543,0.0003848825,0.0003692799],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008112415,"about_ca_system_score_gemma":0.000957678,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1565523,"about_ca_topic_score_gemma":0.2109123,"domain_scores_codex":[0.9997159,0.00004758675,0.000009272132,0.00006483893,0.0001284078,0.00003401909],"domain_scores_gemma":[0.999652,0.00008897347,0.00003431411,0.0000656724,0.0001265468,0.00003240835],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0005919488,0.0003956294,0.1505803,0.0004599146,0.0002619185,0.0004101871,0.0005066301,0.1979884,0.2646764,0.003504769,0.01409494,0.3665289],"study_design_scores_gemma":[0.00005101042,0.0001491916,0.274226,0.0000749987,0.0001331149,0.00022264,0.0005837951,0.6333838,0.069322,0.002591997,0.01914434,0.0001171668],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8519012,0.0009757016,0.1278051,0.0006959498,0.0001198217,0.0001557131,0.007480118,0.002065928,0.008800512],"genre_scores_gemma":[0.8566015,0.000462258,0.1338115,0.0001123452,0.00002789448,0.00008276606,0.006800587,0.000141862,0.001959309],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1565523,"threshold_uncertainty_score":0.3112822,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01677449087423049,"score_gpt":0.2324804971096111,"score_spread":0.2157060062353806,"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."}}