{"id":"W1946682121","doi":"10.1139/cjfr-2013-0125","title":"Potential of UltraCamX stereo images for estimating timber volume and basal area at the plot level in mixed European forests","year":2013,"lang":"en","type":"article","venue":"Canadian Journal of Forest Research","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":86,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Technische Universität München","keywords":"Photogrammetry; Basal area; Point cloud; Remote sensing; Forest inventory; Deciduous; Terrain; Canopy; Aerial survey; Laser scanning; Geography; Digital elevation model; Lidar; Plot (graphics); Environmental science; Forest management; Cartography; Forestry; Computer science; Artificial intelligence; Mathematics; Ecology; Statistics; Archaeology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00132095,0.00007372581,0.0001139354,0.0001287534,0.0002685277,0.00009001134,0.0002793764,0.00003168539,0.0002741617],"category_scores_gemma":[0.0003316766,0.00005310943,0.00004547867,0.0001731696,0.0005392804,0.0001373375,0.00005550984,0.000253275,0.00006387648],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001681997,"about_ca_system_score_gemma":0.0001334048,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.02552693,"about_ca_topic_score_gemma":0.1695283,"domain_scores_codex":[0.9987916,0.0001572551,0.0002793024,0.0001278983,0.000272705,0.000371293],"domain_scores_gemma":[0.9991124,0.0001965345,0.000102827,0.0001719095,0.00009966896,0.0003166421],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003968091,0.00004579893,0.7836542,0.00003726738,0.00002884264,0.00006882953,0.001245531,0.01851214,0.01646764,0.00003826508,0.1138348,0.06602694],"study_design_scores_gemma":[0.0002875858,0.0000798563,0.9824736,0.0000433513,0.000004698655,0.00007371778,0.0001935335,0.01435112,0.0004077245,0.001057683,0.0009680848,0.00005906286],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9929429,0.00003609636,0.002310517,0.001916334,0.00004225795,0.0002824923,0.00001910767,0.000001326215,0.002449017],"genre_scores_gemma":[0.9916919,0.000002619225,0.006618996,0.00002092431,0.00006143057,0.000002539527,0.000002973097,0.00001607904,0.001582567],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1988193,"threshold_uncertainty_score":0.9809622,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04429784413136737,"score_gpt":0.2854671017971462,"score_spread":0.2411692576657789,"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."}}