{"id":"W4414217056","doi":"10.1002/rse2.70029","title":"Investigating boreal forest successional stages in Alaska and Northwest Canada using UAV‐LiDAR and RGB and a community detection network","year":2025,"lang":"en","type":"article","venue":"Remote Sensing in Ecology and Conservation","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"European Research Council; Bundesministerium für Wirtschaft und Energie; Brandenburger Staatsministerium für Wissenschaft, Forschung und Kultur; Deutsche Forschungsgemeinschaft","keywords":"Taiga; Boreal; Ecological succession; Plant community; Forest inventory; Tree line; Community structure","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004479888,0.0001005408,0.0001418538,0.00005394343,0.0005516879,0.00003154271,0.0000249745,0.0001040287,4.460252e-7],"category_scores_gemma":[0.0001279249,0.0001089302,0.000004895901,0.0002505797,0.000312908,0.00009080535,0.00009605599,0.0003076628,7.472125e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001549088,"about_ca_system_score_gemma":0.00008594664,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.7244936,"about_ca_topic_score_gemma":0.9852552,"domain_scores_codex":[0.9990956,0.0002736695,0.0002054056,0.0001987738,0.00005674921,0.0001698175],"domain_scores_gemma":[0.9993498,0.0004228976,0.00007779828,0.00009094318,0.00001172638,0.00004681036],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00000847865,0.000004671447,0.9545403,0.0000163161,0.000003453102,0.00000287961,0.0002558608,0.001163278,0.001151384,0.00001383778,0.00001076313,0.0428288],"study_design_scores_gemma":[0.0002210759,0.00001244998,0.7730953,0.00006333493,0.000007817423,0.00003111805,0.0002618081,0.2244237,0.00006452427,0.001667039,0.00008050167,0.00007125799],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9981403,0.00004206668,0.0004443783,0.0009867729,0.00004765718,0.0001471293,0.00000157418,0.00001049441,0.0001796456],"genre_scores_gemma":[0.9949453,0.00004117579,0.004368767,0.0006044797,0.00001040846,1.245264e-7,0.00001132997,0.000004937101,0.0000134201],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2607617,"threshold_uncertainty_score":0.4442044,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01070469848695644,"score_gpt":0.2309267327831089,"score_spread":0.2202220342961524,"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."}}