{"id":"W2764243986","doi":"10.1139/cjfr-2017-0084","title":"Effects of plot size, stand density, and scan density on the relationship between airborne laser scanning metrics and the Gini coefficient of tree size inequality","year":2017,"lang":"en","type":"article","venue":"Canadian Journal of Forest Research","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National University of Sciences and Technology; European Commission","keywords":"Plot (graphics); Mathematics; Statistics; Gini coefficient; Laser scanning; Density estimation; Correlation coefficient; Remote sensing; Geography; Physics; Laser; Optics; Estimator","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.004746152,0.00007896679,0.0002148501,0.0001099388,0.001022393,0.0001099366,0.0003783095,0.00005604798,0.000007393879],"category_scores_gemma":[0.01227175,0.00004762086,0.00004693674,0.0003109677,0.002190149,0.00007060169,0.0001063087,0.0004642344,0.000001869101],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00014744,"about_ca_system_score_gemma":0.0002291886,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.02093025,"about_ca_topic_score_gemma":0.04701808,"domain_scores_codex":[0.9983442,0.0004471991,0.0002685534,0.0001316469,0.0005612731,0.0002471565],"domain_scores_gemma":[0.9906799,0.008178932,0.00027565,0.0003979931,0.0001364917,0.0003310837],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00004631421,0.00001180583,0.99536,0.00002158247,0.00001760006,0.000009175372,0.0006206902,0.00009274035,0.0001030657,0.001182715,0.0004095093,0.002124761],"study_design_scores_gemma":[0.0005566292,0.0001155795,0.9939464,0.00009150297,0.0000258214,0.000009755716,0.0001447365,0.0001166354,0.001936353,0.002891229,0.000119669,0.00004564175],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9955649,0.00009979676,0.00008139974,0.002871094,0.00002300321,0.000260213,0.000007375383,0.000001103479,0.001091089],"genre_scores_gemma":[0.9997062,0.00001674977,0.0001161054,0.00002118945,0.00003419636,6.031721e-7,2.43421e-7,0.000007264942,0.00009741998],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02608782,"threshold_uncertainty_score":0.9960483,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06651636958555689,"score_gpt":0.3227146506955542,"score_spread":0.2561982811099973,"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."}}