{"id":"W4312464114","doi":"10.1080/01431161.2022.2142077","title":"Integrating Sentinel-1 SAR and Sentinel-2 optical imagery with a crop structure dynamics model to track crop condition","year":2022,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary; Agriculture and Agri-Food Canada","funders":"","keywords":"Normalized Difference Vegetation Index; Remote sensing; Environmental science; Canola; Growing season; Synthetic aperture radar; Leaf area index; Polarimetry; Geography; Agronomy","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0004190333,0.0005326167,0.0002756752,0.0003225395,0.0001527983,0.0004202432,0.0004053894,0.000267567,0.0006302876],"category_scores_gemma":[0.0004823453,0.0002622883,0.0004397407,0.0002746298,0.00008772423,0.0004921401,0.0002451457,0.0003509769,0.0002432308],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000538801,"about_ca_system_score_gemma":0.0006602933,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02055612,"about_ca_topic_score_gemma":0.02645036,"domain_scores_codex":[0.9998832,0.00001980294,0.000006021532,0.00004553745,0.00002714854,0.00001820137],"domain_scores_gemma":[0.9998548,0.00004898071,0.00002569252,0.00001274445,0.00004708058,0.00001056844],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008404683,0.0001497632,0.0230311,0.00003770803,0.0001229434,0.00006166491,0.00006087527,0.9287474,0.01146445,0.001315198,0.001106477,0.03381841],"study_design_scores_gemma":[0.000004533148,0.00001534031,0.002288023,0.000001440317,0.000009630789,0.000006166368,0.000006725112,0.9964542,0.000663779,0.0002237504,0.000320807,0.000005524166],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.720413,0.0001945107,0.2718378,0.0002332511,0.00009542873,0.0001173535,0.001436498,0.001655337,0.004016826],"genre_scores_gemma":[0.9334506,0.00009403894,0.06317203,0.00005311473,0.00001933901,0.00007234978,0.001230307,0.00006873983,0.001839449],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02055612,"threshold_uncertainty_score":0.04087293,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006727583205311761,"score_gpt":0.2388590774529717,"score_spread":0.2321314942476599,"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."}}