{"id":"W2757797739","doi":"10.1016/j.rse.2017.07.031","title":"Tracking crop phenological development using multi-temporal polarimetric Radarsat-2 data","year":2017,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Synthetic Aperture Radar (SAR) Applications and Techniques","field":"Engineering","cited_by":143,"is_retracted":false,"has_abstract":false,"ca_institutions":"Nipissing University; Agriculture and Agri-Food Canada","funders":"Agriculture and Agri-Food Canada; Canadian Space Agency; Northern Ontario Heritage Fund Corporation","keywords":"Phenology; Polarimetry; Remote sensing; Synthetic aperture radar; Normalized Difference Vegetation Index; Environmental science; Canola; Leaf area index; Vegetation (pathology); Agronomy; Geography; Biology; Physics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.000182135,0.0003294692,0.0001876487,0.001390328,0.0001369509,0.0003115147,0.0001791878,0.0002512186,0.0004153902],"category_scores_gemma":[0.0002748123,0.0001417285,0.000143479,0.001050747,0.00006532221,0.0003022415,0.0001921112,0.000167386,0.0002585785],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002264158,"about_ca_system_score_gemma":0.0002709828,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008526486,"about_ca_topic_score_gemma":0.02192746,"domain_scores_codex":[0.9999235,0.000006103458,0.000002673831,0.00003320695,0.00001919208,0.00001545796],"domain_scores_gemma":[0.9998398,0.00002726047,0.00003244772,0.00001375105,0.00006129712,0.0000255174],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005429353,0.0003382498,0.4467009,0.000230539,0.0002004499,0.0003470081,0.0002547937,0.0850291,0.2490063,0.0006049556,0.004434526,0.2123103],"study_design_scores_gemma":[0.00003778962,0.0001032703,0.6162723,0.00001656892,0.00008412924,0.000146635,0.0001167393,0.3613626,0.01808622,0.0002568936,0.003487485,0.0000294476],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9828929,0.0003112107,0.01142457,0.0000734436,0.00003133877,0.00002421003,0.003015173,0.00037662,0.001850476],"genre_scores_gemma":[0.9774065,0.0002273227,0.01739584,0.00002298822,0.00001524718,0.00001991675,0.003939096,0.00002517624,0.0009479958],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008526486,"threshold_uncertainty_score":0.01695371,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0999140805174812,"score_gpt":0.2900416715286488,"score_spread":0.1901275910111676,"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."}}