{"id":"W2801470691","doi":"10.5194/isprs-archives-xlii-3-553-2018","title":"THE EARLY DETECTION OF THE EMERALD ASH BORER (EAB) USING MULTI-SOURCE REMOTELY SENSED DATA","year":2018,"lang":"en","type":"article","venue":"The international 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":"York University","funders":"Natural Sciences and Engineering Research Council of Canada; York University","keywords":"Lidar; Emerald ash borer; Hyperspectral imaging; Remote sensing; Environmental science; Tree health; Tree (set theory); Image resolution; Forest health; Computer science; Geography; Artificial intelligence; Mathematics; Agroforestry; Ecology; Fraxinus","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005688529,0.0002611221,0.000227501,0.001660458,0.0001779843,0.0006135324,0.0003075234,0.0003214816,0.0003174834],"category_scores_gemma":[0.0004689034,0.0001161184,0.0001321116,0.0005233495,0.0001326811,0.0004886765,0.0003762636,0.0002205149,0.0001725982],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002249717,"about_ca_system_score_gemma":0.0001883097,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004476108,"about_ca_topic_score_gemma":0.02382679,"domain_scores_codex":[0.9997659,0.00004174712,0.00001259179,0.00005225372,0.0000999312,0.00002756434],"domain_scores_gemma":[0.9994991,0.0001211016,0.0001226413,0.00003636769,0.0001616053,0.00005931715],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003147595,0.0002079955,0.6014618,0.0002310962,0.00009308782,0.0004782663,0.0003440891,0.00588344,0.2057153,0.0002783219,0.0008219709,0.1841699],"study_design_scores_gemma":[0.00001480472,0.0001413895,0.9181705,0.00003870967,0.00004321457,0.0003148765,0.000651088,0.04520975,0.03314374,0.0001972434,0.002044577,0.00003007206],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9759583,0.000295598,0.02124282,0.00004682105,0.00001209965,0.00004536316,0.0006657383,0.0002791303,0.001454024],"genre_scores_gemma":[0.9651549,0.0001073005,0.03349239,0.00002200164,0.000006796506,0.00001102029,0.000709568,0.00001103288,0.0004851027],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004476108,"threshold_uncertainty_score":0.008900106,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02572090710198784,"score_gpt":0.2625884880912217,"score_spread":0.2368675809892338,"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."}}