{"id":"W6958552799","doi":"10.6084/m9.figshare.27004399.v1","title":"Updating Forest Stand Inventories: Integration of Photo-Interpreted and Airborne Laser Scanning Forest Attributes Using Generic Region Merging Segmentation and kNN Imputation","year":2024,"lang":"en","type":"article","venue":"Figshare","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Imputation (statistics); Segmentation; Random forest; Forest management; Laser scanning; Forest inventory","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004176772,0.0006308263,0.0007608953,0.002185342,0.0008648839,0.00138909,0.001889395,0.0006022081,0.001023568],"category_scores_gemma":[0.009570951,0.0006156332,0.001224219,0.003703763,0.0005130243,0.001073869,0.001167523,0.0008985928,0.0005700377],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00241035,"about_ca_system_score_gemma":0.003201824,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1925268,"about_ca_topic_score_gemma":0.364646,"domain_scores_codex":[0.9982067,0.0004058559,0.0001530815,0.0007137416,0.0003704074,0.0001502243],"domain_scores_gemma":[0.9958372,0.0007789342,0.0005810608,0.001068873,0.001648145,0.00008579741],"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":[0.0002365079,0.000240623,0.3763702,0.0002725486,0.0008069688,0.0002848379,0.001323223,0.2278925,0.00622213,0.002918545,0.008518353,0.3749136],"study_design_scores_gemma":[0.00004184291,0.00004504837,0.1633154,0.00006174823,0.0001907542,0.0001395643,0.0004242866,0.8191774,0.00405148,0.003772287,0.008683756,0.00009649523],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.3844579,0.0005217766,0.5986648,0.0002534088,0.00007913249,0.0003301501,0.008765916,0.003721244,0.003205642],"genre_scores_gemma":[0.5452382,0.0001310393,0.4371533,0.0001115996,0.000025049,0.0001858506,0.01543279,0.0003661248,0.001356131],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1925268,"threshold_uncertainty_score":0.3828123,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0333350187639598,"score_gpt":0.2708561029601009,"score_spread":0.2375210841961411,"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."}}