{"id":"W2014392343","doi":"10.1023/b:gein.0000017745.92969.31","title":"Predicting Forest Age Classes from High Spatial Resolution Remotely Sensed Imagery Using Voronoi Polygon Aggregation","year":2004,"lang":"en","type":"article","venue":"GeoInformatica","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":12,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo; Natural Resources Canada; Canadian Forest Service; Wilfrid Laurier University","funders":"","keywords":"Voronoi diagram; Polygon (computer graphics); Geography; Cartography; Identification (biology); Aggregate (composite); Computer science; Remote sensing; Mathematics; Ecology; Frame (networking)","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":[0.0005216944,0.0005958962,0.0007293188,0.002501209,0.0003770365,0.0008713623,0.0007317205,0.0005544638,0.0009057827],"category_scores_gemma":[0.002176788,0.0004313545,0.0007983769,0.001433202,0.0002227196,0.001027963,0.0004661335,0.0003600518,0.0004392017],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005196827,"about_ca_system_score_gemma":0.0004262038,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02603513,"about_ca_topic_score_gemma":0.0381857,"domain_scores_codex":[0.9997657,0.0000396184,0.00002319419,0.00006035355,0.00005992289,0.00005115232],"domain_scores_gemma":[0.9985994,0.000785398,0.0001604124,0.000113252,0.0002385065,0.0001029127],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008554196,0.0004988633,0.4379289,0.00008758657,0.0002379817,0.0003574198,0.00019143,0.3964168,0.005300759,0.0007158633,0.002208529,0.1552004],"study_design_scores_gemma":[0.00002086637,0.00004473465,0.04971443,0.00000579067,0.00004882918,0.00006382888,0.0001116584,0.9477845,0.0009408621,0.0009971727,0.000254532,0.00001269306],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9661968,0.0002367364,0.03105876,0.00005447954,0.00002341898,0.0000548995,0.001150093,0.0004632888,0.0007615835],"genre_scores_gemma":[0.9822542,0.00007601064,0.01588866,0.000007600755,0.00001585621,0.00002056251,0.001434036,0.00002211626,0.000280958],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02603513,"threshold_uncertainty_score":0.05176717,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01145930332032859,"score_gpt":0.2168998753771763,"score_spread":0.2054405720568477,"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."}}