{"id":"W2980631301","doi":"10.5194/isprs-archives-xlii-4-w18-885-2019","title":"ESTIMATING CANOLA’S BIOPHYSICAL PARAMETERS FROM TEMPORAL, SPECTRAL, AND POLARIMETRIC IMAGERY USING MACHINE LEARNING APPROACHES","year":2019,"lang":"en","type":"article","venue":"˜The œinternational archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Canola; Synthetic aperture radar; Remote sensing; Environmental science; Multispectral image; Earth observation; Polarimetry; Leaf area index; Support vector machine; Geography; Machine learning; Computer science; Agronomy; Satellite; Engineering","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":[0.0004875435,0.0006943406,0.0002500701,0.001529334,0.000213154,0.0007959976,0.000317438,0.0003649331,0.0004979014],"category_scores_gemma":[0.001372617,0.0001856759,0.0003174812,0.00109971,0.0001266397,0.0007720893,0.0002019481,0.000317085,0.0002189144],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006048134,"about_ca_system_score_gemma":0.0005474563,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01917829,"about_ca_topic_score_gemma":0.03335329,"domain_scores_codex":[0.9998605,0.0000382809,0.000008399277,0.00004685254,0.00003116903,0.00001476356],"domain_scores_gemma":[0.9996305,0.0001803399,0.0000463636,0.00002829893,0.0001027543,0.00001177267],"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":[0.0001431387,0.0002575071,0.06473487,0.0001245008,0.0002588146,0.0001352536,0.0001087945,0.6247092,0.01815009,0.001129975,0.0009041765,0.2893437],"study_design_scores_gemma":[0.000003485539,0.00001717932,0.01433565,0.000009461829,0.00002383265,0.00002007531,0.00005859224,0.9819152,0.002364536,0.0006902257,0.0005495994,0.00001205722],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7272162,0.001057119,0.2658378,0.00026758,0.00002635352,0.00006049705,0.0005897333,0.0006313711,0.004313371],"genre_scores_gemma":[0.9581504,0.0002716216,0.0399114,0.00003009874,0.00001301692,0.00002377249,0.0005943345,0.00001857904,0.0009867238],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01917829,"threshold_uncertainty_score":0.03813332,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01999809680145994,"score_gpt":0.2323149726864575,"score_spread":0.2123168758849976,"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."}}