{"id":"W4391564560","doi":"10.3390/rs16040610","title":"Segmentation of Individual Tree Points by Combining Marker-Controlled Watershed Segmentation and Spectral Clustering Optimization","year":2024,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"Saint Mary's University","funders":"National Natural Science Foundation of China; Ministry of Natural Resources of the People's Republic of China","keywords":"Watershed; Segmentation; Cluster analysis; Spectral clustering; Tree (set theory); Artificial intelligence; Pattern recognition (psychology); Computer science; Mathematics; Machine learning; Combinatorics","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":[0.001162319,0.001222853,0.001237725,0.003027678,0.0007405275,0.001309677,0.001255049,0.00117416,0.0009558147],"category_scores_gemma":[0.001847515,0.0006383269,0.001211035,0.002379612,0.0007074694,0.001263496,0.00101055,0.0008019426,0.0008251424],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000725881,"about_ca_system_score_gemma":0.001588154,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007434194,"about_ca_topic_score_gemma":0.010167,"domain_scores_codex":[0.9991533,0.0001240299,0.00006050462,0.0002859668,0.0002828792,0.00009336688],"domain_scores_gemma":[0.9992379,0.000239189,0.000109714,0.0001176246,0.0002601927,0.00003545186],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002096678,0.0002057461,0.003659246,0.0002082087,0.0001405622,0.0001859875,0.0005162503,0.3097003,0.1545572,0.00649137,0.002347668,0.5217777],"study_design_scores_gemma":[0.000009283234,0.0000339045,0.001021097,0.000008592764,0.00002289117,0.00006084342,0.00005271734,0.9722669,0.0223974,0.002806404,0.001294717,0.00002528742],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01524246,0.00004141861,0.9831928,0.00002171812,0.000007841054,0.00006463215,0.00003828792,0.0009764722,0.0004144084],"genre_scores_gemma":[0.118651,0.00007454747,0.8798378,0.00002815138,0.00001231611,0.0001420129,0.0002892248,0.0002885928,0.0006761933],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007434194,"threshold_uncertainty_score":0.01478183,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009259576509814736,"score_gpt":0.2344575124983103,"score_spread":0.2251979359884956,"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."}}