{"id":"W4313456731","doi":"10.1080/17538947.2022.2162136","title":"Mapping common and glossy buckthorns (<i>Frangula alnus</i>and<i>Rhamnus cathartica</i>) using multi-date satellite imagery WorldView-3, GeoEye-1 and SPOT-7","year":2023,"lang":"en","type":"article","venue":"International Journal of Digital Earth","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"Mitacs","keywords":"Understory; Geography; Random forest; Remote sensing; Cartography; Satellite imagery; Support vector machine; Lidar; Forestry; Environmental science; Artificial intelligence; Computer science; Canopy","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002599451,0.0001714417,0.000219619,0.0001240903,0.000082169,0.0004271635,0.0001780108,0.00005233725,0.00001896825],"category_scores_gemma":[0.00009892051,0.000136298,0.0000688779,0.0002143125,0.0002558577,0.0008385436,0.0003115323,0.0002197303,0.00004452819],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003872762,"about_ca_system_score_gemma":0.00001040278,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004680649,"about_ca_topic_score_gemma":0.00003267072,"domain_scores_codex":[0.998613,0.00003568953,0.0003994333,0.0002230059,0.0004969226,0.0002319727],"domain_scores_gemma":[0.9993195,0.0001119221,0.0002364642,0.00009193357,0.00005647455,0.0001837293],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001289315,0.0001723282,0.5741588,0.00003916892,0.0002825318,0.001964928,0.003476487,0.00127966,0.09793902,0.00005297198,0.000587447,0.3199177],"study_design_scores_gemma":[0.0009792655,0.00006165793,0.9639238,0.0002897929,0.00002998267,0.002994581,0.000345783,0.006444202,0.002022674,0.0005358189,0.02203316,0.0003393058],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9971879,0.0004741943,0.0002508554,0.0008328159,0.0002792008,0.00008018348,0.00002716024,0.00002489796,0.0008427775],"genre_scores_gemma":[0.9952919,0.0004629373,0.003502445,0.0001810116,0.0001371457,1.628591e-7,0.000009381984,0.00001633799,0.0003986902],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3897649,"threshold_uncertainty_score":0.5558071,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01794885575365679,"score_gpt":0.255023653036305,"score_spread":0.2370747972826482,"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."}}