{"id":"W2982289032","doi":"10.11575/prism/37092","title":"Remote Sensing Boreal Coarse Woody Debris","year":2019,"lang":"en","type":"dissertation","venue":"PRISM (University of Calgary)","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Debris; Coarse woody debris; Boreal; Remote sensing; Environmental science; Taiga; Snag; Forestry; Hydrology (agriculture); Geography; Geology; Meteorology; Ecology; Geotechnical engineering; Archaeology; Biology; Habitat","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0003894587,0.0005079081,0.0002547631,0.001162463,0.0005903288,0.000979829,0.0005605328,0.0002007255,0.001658007],"category_scores_gemma":[0.0005484823,0.0001833891,0.0002463334,0.001350328,0.0001988133,0.0002532428,0.0003750392,0.0002285328,0.0006616981],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001933509,"about_ca_system_score_gemma":0.002685065,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.8141284,"about_ca_topic_score_gemma":0.9257404,"domain_scores_codex":[0.9996638,0.00001790354,0.00001092808,0.00007984849,0.000160431,0.00006706861],"domain_scores_gemma":[0.99954,0.00003146298,0.00003565013,0.00002421591,0.0003252459,0.00004349268],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003197719,0.0002040973,0.6656747,0.0003609082,0.0000970761,0.0004263762,0.001207596,0.02258844,0.0378602,0.0009304168,0.01742119,0.2529093],"study_design_scores_gemma":[0.00002618142,0.00004574311,0.9303446,0.00006333262,0.000062896,0.0002027763,0.001068633,0.05066705,0.003016966,0.000223434,0.0142283,0.0000501083],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9312754,0.001416696,0.02034928,0.0001701805,0.00007327551,0.0002506831,0.02256793,0.001800355,0.02209616],"genre_scores_gemma":[0.9484732,0.0005063772,0.03212784,0.00009290595,0.00001688718,0.00007279085,0.01341199,0.00007055454,0.005227583],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8141284,"threshold_uncertainty_score":0.3739324,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006818295277094143,"score_gpt":0.1996070265677875,"score_spread":0.1927887312906934,"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."}}