{"id":"W2058688210","doi":"10.1139/x05-030","title":"The application of digital photogrammetry and image analysis techniques to derive tree and stand characteristics","year":2005,"lang":"en","type":"article","venue":"Canadian Journal of Forest Research","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Aerial photography; Photogrammetry; Forest inventory; Basal area; Aerial photos; Remote sensing; Tree (set theory); Aerial image; Digital photography; Forestry; Computer science; Stand development; Environmental science; Geography; Forest management; Photography; Computer vision; Mathematics; Image (mathematics)","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":[0.0005870577,0.0000458769,0.00009733325,0.0002028396,0.0002188083,0.0001477648,0.0001489615,0.00002759869,0.000008849854],"category_scores_gemma":[0.0001310857,0.00003355506,0.0000284684,0.0005625047,0.0004580783,0.00009191039,0.00002980512,0.0001417473,0.000004248612],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001076704,"about_ca_system_score_gemma":0.00007418285,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004511969,"about_ca_topic_score_gemma":0.1047592,"domain_scores_codex":[0.9993075,0.00002822962,0.0001777295,0.00009101387,0.0002084128,0.0001871542],"domain_scores_gemma":[0.9992269,0.0001045588,0.00006810095,0.0001496886,0.00008308754,0.0003676258],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00001450591,0.00001073363,0.2416909,0.000003565696,0.00003831059,0.000004113776,0.0004081938,0.00001193061,0.003481431,0.0000941969,0.000957789,0.7532843],"study_design_scores_gemma":[0.00007962694,0.0001361591,0.8853633,0.00001127241,0.00003722571,0.00003585558,0.000372939,0.001085214,0.002330871,0.0006169627,0.1098541,0.00007639963],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9889904,0.00008163451,0.006978599,0.001681693,0.000003428382,0.0001462629,0.00001886795,0.000001785538,0.002097368],"genre_scores_gemma":[0.9975209,0.00005655269,0.002274694,0.0000131783,0.00002947503,0.00000142706,0.000001635245,0.000004677191,0.00009748886],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7532079,"threshold_uncertainty_score":0.9115766,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01447052359779502,"score_gpt":0.2953683070218381,"score_spread":0.2808977834240431,"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."}}