{"id":"W1846753643","doi":"10.1139/x11-083","title":"Combining tree height samples produced by airborne laser scanning and stand management records to estimate plot volume in<i>Eucalyptus</i>plantations","year":2011,"lang":"en","type":"article","venue":"Canadian Journal of Forest Research","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Eucalyptus; Canopy; Mean squared error; Mathematics; Laser scanning; Tree (set theory); Tree canopy; Volume (thermodynamics); Forest inventory; Remote sensing; Forest management; Statistics; Environmental science; Forestry; Geography; Laser; Ecology","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":[0.001032374,0.00009328422,0.0001375161,0.000345519,0.0003136285,0.00009029872,0.0002528036,0.00003651293,0.0001348909],"category_scores_gemma":[0.00009980975,0.00008786007,0.00002240889,0.0005507926,0.0002227436,0.0001537359,0.00004979003,0.0002950821,0.00004730458],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002953254,"about_ca_system_score_gemma":0.0001444055,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.02422276,"about_ca_topic_score_gemma":0.1735365,"domain_scores_codex":[0.9986224,0.00009318956,0.0002718797,0.0002183793,0.0003127657,0.0004814068],"domain_scores_gemma":[0.9989232,0.00005412828,0.00005971638,0.0001965345,0.00005249109,0.0007138691],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00006056817,0.00006436177,0.9165422,0.0000263593,0.00003367623,0.000242019,0.004476686,0.0008547727,0.0007148855,0.0004187055,0.03841931,0.03814649],"study_design_scores_gemma":[0.0003625574,0.0002023716,0.967379,0.0001274321,0.00001039319,0.00004704211,0.0005794778,0.0003198791,0.0008200738,0.001136776,0.02887937,0.0001355998],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9873489,0.00009372755,0.0003458696,0.001136118,0.00004652716,0.0002470081,0.00001675188,0.000004913716,0.0107602],"genre_scores_gemma":[0.9926081,0.00002820873,0.006154196,0.00003925846,0.00002141078,0.000004802979,0.000004762154,0.00001674605,0.001122509],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1493138,"threshold_uncertainty_score":0.982275,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04880759900422322,"score_gpt":0.3033548027896503,"score_spread":0.2545472037854271,"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."}}