{"id":"W3005037815","doi":"","title":"An Integrated System for Estimating Forest Basal Area from Spherical Images","year":2020,"lang":"en","type":"article","venue":"Mathematical and Computational Forestry & Natural-Resource Sciences (MCFNS)","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Basal area; Forest inventory; Visibility; Sample (material); Understory; Software; Computer science; Sampling (signal processing); Geography; Forestry; Environmental science; Remote sensing; Mathematics; Computer vision; Canopy; Forest management; Filter (signal processing); Archaeology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000240075,0.0002211943,0.00026104,0.00002213508,0.0005822598,0.0002720873,0.0003946833,0.00007618516,0.0001124346],"category_scores_gemma":[0.0001867084,0.0001626949,0.00008104266,0.0003931438,0.0007078758,0.0002284441,0.0001249338,0.0001948302,0.00007927469],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004864112,"about_ca_system_score_gemma":0.00002821657,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008248117,"about_ca_topic_score_gemma":0.00000560361,"domain_scores_codex":[0.9981329,0.00005815677,0.0003656943,0.0006038991,0.000524045,0.0003152679],"domain_scores_gemma":[0.9987696,0.0006406318,0.0001264019,0.000140199,0.00002944668,0.0002937395],"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":[0.0001321486,0.0002857771,0.005336145,0.0001932063,0.00005458995,0.00001650367,0.002262441,0.9133489,0.003434217,0.01834269,0.006299101,0.05029427],"study_design_scores_gemma":[0.0002399571,0.0001106095,0.003019109,0.00005412436,0.00002202787,0.00001792021,0.0008605359,0.9639357,0.000132641,0.03078323,0.000612237,0.000211862],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5296588,0.00002855391,0.466441,0.001873414,0.00004893484,0.0003239762,0.00004551547,0.0001541625,0.001425699],"genre_scores_gemma":[0.6660209,1.423036e-7,0.3334167,0.0003444516,0.00009727725,0.000008883047,0.00006342209,0.00001095381,0.00003725398],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1363621,"threshold_uncertainty_score":0.6634507,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01527244019814721,"score_gpt":0.247795667769287,"score_spread":0.2325232275711398,"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."}}