{"id":"W2120148922","doi":"10.1109/igarss.2006.283","title":"The FIFEDOM (Frequent Image Frames Enhanced Digital Orthorectified Mapping) Camera for Automatic Mapping of Tree Species and Structures","year":2006,"lang":"en","type":"article","venue":"","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Orthophoto; Remote sensing; Computer vision; Computer science; Artificial intelligence; Digital camera; Tree (set theory); Digital surface; Image resolution; Frame (networking); Frame rate; Field of view; Geography; Mathematics; Lidar","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":[0.000999604,0.0005199203,0.000559823,0.001733658,0.0005047291,0.0006337825,0.0007049076,0.0006514539,0.01071182],"category_scores_gemma":[0.0005773063,0.0004070428,0.0004609494,0.0007847784,0.0003392189,0.0009049782,0.0005534574,0.0007848918,0.001518321],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006081991,"about_ca_system_score_gemma":0.001045592,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004810965,"about_ca_topic_score_gemma":0.01391891,"domain_scores_codex":[0.999566,0.00004407518,0.00001804211,0.00009032771,0.0002167,0.0000647967],"domain_scores_gemma":[0.9995833,0.00006172788,0.00002794214,0.00007189489,0.0001950664,0.00006016165],"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":[0.0005992412,0.0002421008,0.007520238,0.0005731206,0.00006989259,0.0003735332,0.0001681045,0.002890638,0.3928728,0.009388735,0.04382774,0.541474],"study_design_scores_gemma":[0.0006637633,0.002059015,0.0931031,0.0003359146,0.0002808161,0.006838577,0.0002738849,0.1568599,0.3189613,0.003134198,0.4170243,0.0004652643],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09451155,0.001935826,0.8404954,0.0004300367,0.0005834288,0.002189159,0.007650334,0.003965273,0.04823899],"genre_scores_gemma":[0.1044766,0.0007651937,0.8685613,0.0002373591,0.00008873871,0.0005621651,0.005132228,0.0001371548,0.02003931],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01071182,"threshold_uncertainty_score":0.03583461,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008564499545838041,"score_gpt":0.2131195552879948,"score_spread":0.2045550557421567,"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."}}