{"id":"W2967537381","doi":"10.1007/s13595-019-0852-9","title":"The utility of terrestrial photogrammetry for assessment of tree volume and taper in boreal mixedwood forests","year":2019,"lang":"en","type":"article","venue":"Annals of Forest Science","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Wood Council; Alberta Ministry of Agriculture and Forestry; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Agriculture and Forestry","keywords":"Photogrammetry; Pinus contorta; Point cloud; Taiga; Abies balsamea; Forest inventory; Environmental science; Context (archaeology); Remote sensing; Boreal; Volume (thermodynamics); Tree (set theory); Forestry; Forest management; Geography; Balsam; Ecology; Agroforestry; Mathematics; Computer science; Biology; Botany","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008276654,0.0003913293,0.0001801829,0.001971947,0.0003240844,0.0008104082,0.0004812592,0.0001665435,0.000563976],"category_scores_gemma":[0.002028573,0.0001357249,0.0001714513,0.001024705,0.0003065498,0.0004550454,0.0004730769,0.0001358821,0.0001326065],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006805761,"about_ca_system_score_gemma":0.0003687225,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03827729,"about_ca_topic_score_gemma":0.1019669,"domain_scores_codex":[0.9994246,0.0001089815,0.00002399854,0.0001232608,0.0002926432,0.00002663037],"domain_scores_gemma":[0.9987602,0.0003750386,0.0002588591,0.0001329347,0.0004134391,0.00005954196],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002704031,0.00007408684,0.7569088,0.0001611485,0.0001131397,0.0001359213,0.0006909019,0.01849155,0.03962557,0.0001968469,0.0003024731,0.1830291],"study_design_scores_gemma":[0.000006060235,0.00009588594,0.9608487,0.00002395418,0.00002535203,0.000189006,0.0005373087,0.03230016,0.005252475,0.000133715,0.0005631508,0.00002410509],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9855847,0.0002336014,0.01195212,0.00001081342,0.000004784088,0.00004066925,0.0004398973,0.0001231414,0.001610249],"genre_scores_gemma":[0.9891998,0.00005993302,0.01028666,0.00000560549,0.000002818502,0.00001282201,0.0002511741,0.000009712399,0.0001715073],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03827729,"threshold_uncertainty_score":0.07610899,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03152242481723787,"score_gpt":0.3239315478484942,"score_spread":0.2924091230312563,"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."}}