{"id":"W3029109565","doi":"10.1080/07038992.2020.1768515","title":"Object-Oriented Automatic Identification of Forest Gaps Using Digital Orthophoto Maps and LiDAR Data","year":2020,"lang":"en","type":"article","venue":"Canadian Journal of Remote Sensing","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Lidar; Orthophoto; Segmentation; Remote sensing; Computer science; Artificial intelligence; Geography; Cartography","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002567184,0.0001115115,0.0002022809,0.00009128174,0.0001585409,0.0001065867,0.0002007321,0.00005192727,0.00001476062],"category_scores_gemma":[0.0003245402,0.0001122497,0.00004509613,0.0003766133,0.0002114365,0.0003539961,0.0000608529,0.0001572075,0.00001218771],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001126199,"about_ca_system_score_gemma":0.0002001644,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004824169,"about_ca_topic_score_gemma":0.004674488,"domain_scores_codex":[0.9988222,0.00003691389,0.0005056521,0.0002139747,0.0002168182,0.0002044559],"domain_scores_gemma":[0.9986515,0.00003734929,0.0004187247,0.0003408327,0.00005144881,0.0005001589],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002219239,0.00001289871,0.01453424,0.0000894172,0.0000853127,0.0002510063,0.00463139,0.001522997,0.06284691,0.00001522265,0.001356123,0.9146323],"study_design_scores_gemma":[0.0007078682,0.0001238257,0.03497626,0.000379292,0.0001999254,0.001961519,0.002000971,0.9201191,0.005399037,0.0007786783,0.03285848,0.0004950598],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9597477,0.00008194392,0.03886971,0.0004939068,0.0001010448,0.0001088818,0.00004104217,0.000008471239,0.0005473439],"genre_scores_gemma":[0.9537252,0.000006777489,0.04605221,0.00009868884,0.00006636063,1.930907e-9,0.00001842827,0.00001735202,0.00001499081],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9185961,"threshold_uncertainty_score":0.7292733,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02235816005915564,"score_gpt":0.2357020388549992,"score_spread":0.2133438787958436,"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."}}