{"id":"W1989033557","doi":"10.2316/journal.206.2013.2.206-3800","title":"GENERAL VEGETATION DETECTION USING AN INTEGRATED VISION SYSTEM","year":2013,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Remote Sensing and Land Use","field":"Earth and Planetary Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Vegetation (pathology); Computer vision; Computer science; Artificial intelligence; Remote sensing; Geography; Medicine; Pathology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002667738,0.0005888175,0.0008370784,0.001174492,0.0003816025,0.0007802497,0.0008673799,0.0008403327,0.002866556],"category_scores_gemma":[0.0004309033,0.000482212,0.0004835044,0.0007735714,0.0001886677,0.000814225,0.000759003,0.0005739639,0.001110236],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002824447,"about_ca_system_score_gemma":0.0005302419,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001959414,"about_ca_topic_score_gemma":0.00356685,"domain_scores_codex":[0.9995949,0.00002685569,0.00001204373,0.0001541634,0.0001506741,0.0000614962],"domain_scores_gemma":[0.9996891,0.00004200032,0.00002038559,0.00004084297,0.0001647331,0.00004296813],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0007152659,0.0002911398,0.004876164,0.0001802465,0.0001353976,0.0001348194,0.00008785578,0.005624344,0.5858048,0.0008265259,0.002346939,0.3989765],"study_design_scores_gemma":[0.0002620848,0.001472693,0.06629401,0.00007282564,0.0005627318,0.001504864,0.0001161392,0.6580762,0.2567703,0.002350799,0.01233627,0.0001812303],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2110781,0.0006560567,0.7740371,0.0001261999,0.0002447566,0.0001833454,0.0004771308,0.006264787,0.006932592],"genre_scores_gemma":[0.6562668,0.0002697448,0.3376156,0.0001883776,0.00008930633,0.0001127484,0.0005670739,0.0001332807,0.004756982],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002866556,"threshold_uncertainty_score":0.009589612,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01319967427085003,"score_gpt":0.2402529390005627,"score_spread":0.2270532647297126,"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."}}