{"id":"W1981697813","doi":"10.1109/jstars.2013.2244131","title":"Foreward to the Special Issue on Multi-Scale Forestry Applications Supported by Remote Sensing and Spatial Information Systems","year":2013,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada","funders":"","keywords":"Remote sensing; Lidar; Computer science; Scale (ratio); Cover (algebra); Focus (optics); Geography; Cartography; Engineering","routes":{"ca_aff":true,"ca_fund":false,"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":[0.002267655,0.001692329,0.002585516,0.003721318,0.001510957,0.007505099,0.001825576,0.003357884,0.0807519],"category_scores_gemma":[0.004946026,0.0004777277,0.001589063,0.003498195,0.0007689759,0.005004183,0.001984637,0.005035468,0.05366596],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008510493,"about_ca_system_score_gemma":0.001272463,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000565487,"about_ca_topic_score_gemma":0.001562015,"domain_scores_codex":[0.9983774,0.0001821499,0.0001540691,0.0003698982,0.000737165,0.0001791531],"domain_scores_gemma":[0.9914138,0.002212717,0.0004137449,0.0005447047,0.003745667,0.001669358],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002717035,0.00004119778,0.0001725299,0.0002383084,0.00001612839,0.00007196623,0.00001277682,0.00006552236,0.000340991,0.0007532705,0.9636808,0.0345794],"study_design_scores_gemma":[0.000008478869,0.00004937723,0.0005940644,0.0001138687,0.00001939091,0.0001184087,0.00002987008,0.0002835989,0.0001928424,0.001023558,0.9975542,0.00001233829],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"editorial","genre_scores_codex":[0.0005225566,0.0177568,0.004532856,0.02174943,0.92359,0.0001305744,0.0006240944,0.0005104234,0.03058318],"genre_scores_gemma":[0.002591098,0.02208356,0.002752762,0.008939356,0.8281185,0.0001107189,0.001484914,0.0006156052,0.1333035],"genre_candidate":"editorial","genre_consensus":"editorial","teacher_disagreement_score":0.0807519,"threshold_uncertainty_score":0.270142,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01224077807140429,"score_gpt":0.2199763299054994,"score_spread":0.2077355518340951,"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."}}