{"id":"W2093111773","doi":"10.5589/m13-012","title":"Patterns of covariance between airborne laser scanning metrics and Lorenz curve descriptors of tree size inequality","year":2013,"lang":"en","type":"article","venue":"Canadian Journal of Remote Sensing","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Statistics; Mathematics; Lorenz curve; Covariance; Percentile; Gini coefficient; Econometrics; Inequality; Economic inequality","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":[0.0005781864,0.0001313089,0.0003407858,0.0001648952,0.0001006318,0.00003601392,0.0001452219,0.00008302138,0.00005746703],"category_scores_gemma":[0.0004630559,0.0001246119,0.00007984715,0.0004455093,0.0002005765,0.0001795944,0.00002771835,0.0002216008,0.000005449312],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001747201,"about_ca_system_score_gemma":0.0001313894,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1051819,"about_ca_topic_score_gemma":0.01625943,"domain_scores_codex":[0.9986451,0.0001072658,0.0005701185,0.0001586343,0.0002507144,0.0002681247],"domain_scores_gemma":[0.9984234,0.0002136288,0.0005405633,0.0002317967,0.0001209813,0.0004696343],"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":[0.000005393963,0.000006465797,0.163672,0.00003255517,0.00005313205,0.00002263373,0.001329992,0.000301323,0.005039488,0.000006847954,0.0006174372,0.8289127],"study_design_scores_gemma":[0.0004881772,0.0001109798,0.9728312,0.0003208693,0.00009546387,0.0001278241,0.000723845,0.005056398,0.01651109,0.0008416439,0.002628378,0.0002641285],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9678028,0.00007253085,0.03087975,0.0004046076,0.00009733007,0.00009850093,0.00001375041,0.000003576163,0.0006271303],"genre_scores_gemma":[0.9328633,0.00001173059,0.06692665,0.00006930381,0.00006401529,5.927244e-9,0.000001465058,0.00001496973,0.00004856278],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8286486,"threshold_uncertainty_score":0.9073142,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02349880125585429,"score_gpt":0.2331313143596029,"score_spread":0.2096325131037486,"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."}}