{"id":"W2048684815","doi":"10.5558/tfc79421-3","title":"Canada's National Forest Inventory: What can it tell us about old growth?","year":2003,"lang":"en","type":"article","venue":"The Forestry Chronicle","topic":"Forest ecology and management","field":"Environmental Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Sport Centre Pacific; Natural Resources Canada; Canadian Forest Service","funders":"","keywords":"Forest inventory; Maturity (psychological); Distribution (mathematics); National forest; Perpetual inventory; Forest management; Inventory management; Geography; Environmental resource management; Forestry; Environmental science; Operations management; Economics; Mathematics; Political science; Inventory theory","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003423122,0.0001544738,0.0001042252,0.00001619798,0.0003471689,0.00005743756,0.0004367354,0.00006460192,0.002408201],"category_scores_gemma":[0.00008754442,0.00012024,0.00004588895,0.000171808,0.0003048492,0.0002595292,0.0001659188,0.0001724977,0.0002810569],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001045181,"about_ca_system_score_gemma":0.0003166913,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.23269,"about_ca_topic_score_gemma":0.9041917,"domain_scores_codex":[0.9985993,0.00006843844,0.0001784699,0.0002654789,0.0004271101,0.0004612269],"domain_scores_gemma":[0.9994516,0.00004848032,0.00007728882,0.0002912763,0.00001663392,0.0001147392],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.00001711942,0.0001153857,0.300102,0.00002018376,0.00005348452,0.00003382247,0.0002151078,0.02202024,0.0000413815,0.1431467,0.5341069,0.0001276562],"study_design_scores_gemma":[0.0006980789,0.00007920058,0.5920408,0.00002053734,0.00002615004,0.0000242059,0.0002168596,0.001801288,0.000546327,0.02406814,0.3801633,0.0003151052],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9572835,0.0002033815,0.00002796834,0.004678451,0.0005138886,0.0003531875,0.00001074566,0.00003306827,0.03689579],"genre_scores_gemma":[0.9858561,0.00006941825,0.00003319563,0.007899303,0.0000527037,0.00004844211,0.00001247136,0.00001591972,0.006012465],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6715018,"threshold_uncertainty_score":0.9985037,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008526766281462978,"score_gpt":0.201364757794817,"score_spread":0.192837991513354,"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."}}