{"id":"W2891734946","doi":"10.1007/s10021-018-0297-2","title":"At What Scales and Why Does Forest Structure Vary in Naturally Dynamic Boreal Forests? An Analysis of Forest Landscapes on Two Continents","year":2018,"lang":"en","type":"article","venue":"Ecosystems","topic":"Ecology and Vegetation Dynamics Studies","field":"Environmental Science","cited_by":22,"is_retracted":false,"has_abstract":false,"ca_institutions":"Canadian Forest Service","funders":"Emil Aaltosen Säätiö; Academy of Finland","keywords":"Spatial variability; Taiga; Variation (astronomy); Spatial ecology; Scale (ratio); Boreal; Ecology; Physical geography; Temporal scales; Environmental science; Canopy; Geography; Biology; Cartography","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":[0.0005291788,0.0001738868,0.0003003585,0.001111026,0.0008440961,0.001144862,0.0004053585,0.0003509851,0.000893719],"category_scores_gemma":[0.001513735,0.0001704646,0.0005660621,0.001530274,0.001463528,0.0009537925,0.0007016664,0.0002537351,0.00006617597],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006622063,"about_ca_system_score_gemma":0.0003725952,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04568872,"about_ca_topic_score_gemma":0.09671494,"domain_scores_codex":[0.9996994,0.0001057905,0.00001193927,0.00007497412,0.00002709629,0.00008096937],"domain_scores_gemma":[0.9991209,0.0003410086,0.0002223699,0.00008879467,0.00009196153,0.0001350238],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001017849,0.00004334002,0.9908653,0.00001887632,0.0001469703,0.0001392937,0.001611353,0.0006580877,0.001279571,0.0005439428,0.0001239845,0.004467423],"study_design_scores_gemma":[0.000001285016,0.000009655801,0.9984982,0.000001889549,0.00001235162,0.00002639365,0.0009014671,0.0003687516,0.00001585028,0.00008045438,0.00008166955,0.000002113782],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9995825,0.00006859782,0.00005459611,0.000021677,7.023897e-7,0.000001344489,0.00002733133,0.000001078809,0.0002421423],"genre_scores_gemma":[0.999778,0.00003094058,0.00007294671,0.000006735779,0.000001633255,0.000001773329,0.00006214893,0.000001147451,0.00004462239],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04568872,"threshold_uncertainty_score":0.09084558,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003508107721223252,"score_gpt":0.2322171527930521,"score_spread":0.2287090450718288,"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."}}