{"id":"W2417299185","doi":"10.1016/b978-0-08-098222-9.00004-2","title":"Forest Monitoring Methods in the United States and Canada","year":2013,"lang":"en","type":"book-chapter","venue":"Developments in environmental science","topic":"Fire effects on ecosystems","field":"Environmental Science","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"Cumulative Environmental Management Association","funders":"","keywords":"Forest health; National forest; Warning system; Forest inventory; Geography; Environmental protection; Environmental resource management; Early warning system; Oil sands; Environmental science; Environmental planning; Forestry; Forest management; Engineering; Cartography","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001567377,0.0003927558,0.0002798705,0.0001965821,0.0002277275,0.0000927149,0.001031291,0.0001214999,0.0004609874],"category_scores_gemma":[0.00003227722,0.0003099665,0.00001896473,0.0002221183,0.0008960217,0.0003332724,0.0007811708,0.0004392503,0.0001582963],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003053416,"about_ca_system_score_gemma":0.00009266959,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.4380553,"about_ca_topic_score_gemma":0.3064676,"domain_scores_codex":[0.9968979,0.00009237776,0.0004704421,0.0007728903,0.001145598,0.0006208099],"domain_scores_gemma":[0.9990454,0.0002402884,0.0001784028,0.000379939,0.000001893044,0.0001540844],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000008766924,0.00006989154,0.9247897,0.00003589009,0.00001574385,0.0001803428,0.002725423,0.001211281,0.00245502,0.0002768789,0.0009213199,0.0673098],"study_design_scores_gemma":[0.0002112482,0.00002771603,0.9006878,0.0001392587,0.000005319595,0.00002827935,0.0003776242,0.001034641,0.0003989188,0.0004290252,0.09607387,0.0005863246],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9219657,0.00009282714,0.00001693445,0.00009681928,0.0004644263,0.0009825388,0.00001470076,0.00001281623,0.07635329],"genre_scores_gemma":[0.957092,0.0008041996,0.01194665,0.0005557203,0.00003552524,0.000167498,0.00005300043,0.00007120869,0.02927413],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1315877,"threshold_uncertainty_score":0.9999353,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01047689926332985,"score_gpt":0.233734207653073,"score_spread":0.2232573083897431,"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."}}