{"id":"W2107620039","doi":"10.1139/cjfr-2014-0203","title":"Estimation of standing wood volume in forest compartments by exploiting airborne laser scanning information: model-based, design-based, and hybrid perspectives","year":2014,"lang":"en","type":"article","venue":"Canadian Journal of Forest Research","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":60,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"European Commission","keywords":"Estimator; Stratum; Volume (thermodynamics); Statistics; Laser scanning; Environmental science; Lidar; Mathematics; Remote sensing; Geology; Laser; Optics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003142192,0.0007534634,0.0008494711,0.002307271,0.000153251,0.001135114,0.0006894597,0.0005972756,0.0002492967],"category_scores_gemma":[0.007276942,0.0005587398,0.001140995,0.00110062,0.0006162557,0.0009071793,0.0009222557,0.00029644,0.00007859789],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006064597,"about_ca_system_score_gemma":0.0006374752,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002274845,"about_ca_topic_score_gemma":0.002283422,"domain_scores_codex":[0.997596,0.001285589,0.0001019202,0.0003441689,0.0005866692,0.00008575072],"domain_scores_gemma":[0.9942822,0.004056435,0.0006693554,0.0005386415,0.0003815841,0.00007179064],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002025544,0.0001411895,0.04341402,0.0001799828,0.0002848485,0.00008921138,0.0001387406,0.7414345,0.01394805,0.003738174,0.0001100511,0.1963186],"study_design_scores_gemma":[0.00001221777,0.0002858938,0.01551131,0.00001691271,0.00008490483,0.0001461444,0.00003957002,0.9748147,0.005422096,0.003220745,0.0004014847,0.00004408196],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1080173,0.0004709444,0.890634,0.00004859951,0.00000623363,0.00004182541,0.00008113095,0.0001687485,0.0005312031],"genre_scores_gemma":[0.8407629,0.000313017,0.1582366,0.00001782795,0.00002211846,0.00008779344,0.0002038613,0.00003485591,0.000320929],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003142192,"threshold_uncertainty_score":0.01661772,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03035234839621347,"score_gpt":0.2809070083904245,"score_spread":0.2505546599942111,"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."}}