{"id":"W2885006302","doi":"10.3390/ijgi7080294","title":"An Object-Based Image Analysis Workflow for Monitoring Shallow-Water Aquatic Vegetation in Multispectral Drone Imagery","year":2018,"lang":"en","type":"article","venue":"ISPRS International Journal of Geo-Information","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":89,"is_retracted":false,"has_abstract":true,"ca_institutions":"Fleming College; Trent University","funders":"","keywords":"Multispectral image; Workflow; Computer science; Drone; Remote sensing; Vegetation (pathology); Artificial intelligence; Software; Cartography; Geography; Database","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007929348,0.0001191,0.0001639978,0.0005115535,0.0001123652,0.0002140272,0.0003148571,0.00006061173,0.00009774301],"category_scores_gemma":[0.0001109142,0.0001018069,0.000150575,0.000299647,0.00009242607,0.002792671,0.00002245243,0.0001316321,0.0001682763],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003767129,"about_ca_system_score_gemma":0.0000275053,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002446176,"about_ca_topic_score_gemma":0.0001618387,"domain_scores_codex":[0.9984201,0.00005186592,0.0006704224,0.0001050712,0.0005524106,0.0002001835],"domain_scores_gemma":[0.9991213,0.00007771426,0.0003695689,0.0001515847,0.0002013037,0.00007854165],"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":[0.001778382,0.0007455808,0.1564912,0.00004681765,0.0008566537,0.00002141771,0.01567651,0.2285819,0.2318902,0.0001404447,0.0005943184,0.3631766],"study_design_scores_gemma":[0.001723286,0.0002255411,0.3899074,0.00007135508,0.0001544095,0.00001861742,0.0003563299,0.5345418,0.07084727,0.0008242417,0.001070023,0.0002596928],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6829339,0.000003326148,0.3153898,0.0004718567,0.0004256428,0.000148279,0.00000558609,0.00001554469,0.0006060172],"genre_scores_gemma":[0.936712,0.000003576922,0.06280633,0.0001353704,0.0002436005,0.000004804448,0.00007541454,0.000006699877,0.00001221369],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3629169,"threshold_uncertainty_score":0.4151565,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006371756479457254,"score_gpt":0.2718130461167544,"score_spread":0.2654412896372972,"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."}}