{"id":"W2523234571","doi":"10.1080/07038992.2016.1228447","title":"Introduction to Special Issue on Remote Sensing for Advanced Forest Inventory","year":2016,"lang":"en","type":"article","venue":"Canadian Journal of Remote Sensing","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Forest Service","funders":"","keywords":"Sustainable forest management; Environmental resource management; Forest inventory; Geography; Forest management; Set (abstract data type); Environmental planning; Business; Regional science; Remote sensing; Computer science; Forestry; Environmental science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":false,"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":[0.002998548,0.001816549,0.001675853,0.003736293,0.001020179,0.004215632,0.002247442,0.003924013,0.1280454],"category_scores_gemma":[0.008312142,0.0005878906,0.001081508,0.001827617,0.0008589976,0.00452254,0.002054687,0.004919895,0.09806164],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001038748,"about_ca_system_score_gemma":0.001476896,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002294391,"about_ca_topic_score_gemma":0.004905901,"domain_scores_codex":[0.9984755,0.000170897,0.0001922086,0.0003416869,0.0007202465,0.00009944868],"domain_scores_gemma":[0.9893791,0.002435188,0.0006603881,0.0005329539,0.00537779,0.001614676],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001118607,0.00001125076,0.00006906212,0.0001082668,0.000003897675,0.00002499116,0.000005500693,0.000031917,0.0001541838,0.0003150374,0.9827679,0.01649682],"study_design_scores_gemma":[0.00000450687,0.00001553248,0.0004855747,0.0001232162,0.000004500871,0.0000815785,0.000009332738,0.00006268677,0.00005666703,0.0006308526,0.9985186,0.000006990031],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"editorial","genre_scores_codex":[0.0002114385,0.02037168,0.003247435,0.04098943,0.9021593,0.0001849822,0.001911281,0.0005929196,0.03033157],"genre_scores_gemma":[0.00127264,0.02406408,0.00202142,0.02566123,0.8235592,0.0001583196,0.002173014,0.0008699429,0.12022],"genre_candidate":"editorial","genre_consensus":"editorial","teacher_disagreement_score":0.1280454,"threshold_uncertainty_score":0.4283544,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01076031252486483,"score_gpt":0.2301121230010755,"score_spread":0.2193518104762106,"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."}}