{"id":"W2781910890","doi":"10.1080/01431161.2017.1420940","title":"Combining image processing and machine learning to identify invasive plants in high-resolution images","year":2018,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"Thompson Rivers University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial intelligence; Random forest; Thresholding; Computer science; Pattern recognition (psychology); Classifier (UML); Image processing; Contextual image classification; Feature extraction; Computer vision; Feature selection; Image (mathematics)","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":[0.001102667,0.0006920347,0.0005635021,0.001374986,0.0002318477,0.000672119,0.0004467163,0.0006519451,0.0004908071],"category_scores_gemma":[0.001533572,0.0002159078,0.0006236397,0.0009355119,0.000344091,0.0008746001,0.0002722981,0.000452738,0.0005036432],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002234547,"about_ca_system_score_gemma":0.000317553,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001455523,"about_ca_topic_score_gemma":0.003242785,"domain_scores_codex":[0.9994979,0.0001209893,0.00002650947,0.0001248209,0.0001673585,0.00006245386],"domain_scores_gemma":[0.9991403,0.0003927431,0.0001215425,0.00009897927,0.0002263354,0.00002024629],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002778249,0.0003667983,0.01255562,0.0002766982,0.000205913,0.0001856494,0.000157528,0.06797181,0.2120687,0.0007247081,0.0008308867,0.7043778],"study_design_scores_gemma":[0.00001725305,0.000337013,0.03440054,0.0000349341,0.000136428,0.0003707185,0.0001707933,0.8603175,0.1008179,0.001495026,0.001846974,0.00005491316],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.271608,0.0007516585,0.722809,0.0001931436,0.00006062018,0.0001423577,0.0001285054,0.001559909,0.002746805],"genre_scores_gemma":[0.5633577,0.0003767859,0.4349029,0.00009022024,0.00005049659,0.00006708155,0.0002366365,0.00006364942,0.0008545335],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001455523,"threshold_uncertainty_score":0.00583154,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0114692369943575,"score_gpt":0.2680761452632907,"score_spread":0.2566069082689332,"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."}}