{"id":"W2082296761","doi":"10.5558/tfc87023-1","title":"An ecological land classification approach to modeling the production of forest biomass","year":2011,"lang":"en","type":"article","venue":"The Forestry Chronicle","topic":"Forest ecology and management","field":"Environmental Science","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"Nipissing University","funders":"Ontario Ministry of Research and Innovation; Northern Ontario Heritage Fund Corporation; Nipissing University","keywords":"Forest management; Environmental resource management; Forest inventory; Site index; Biomass (ecology); Sustainable forest management; Production (economics); Productivity; Environmental science; Agroforestry; Geography; Forestry; Ecology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004614017,0.00007453699,0.00006631976,0.00001263776,0.0002046423,0.000008058923,0.0004270412,0.00004919237,0.000151941],"category_scores_gemma":[0.00002581337,0.00003964557,0.00002718076,0.0001512585,0.0002375494,0.0001248402,0.0001391645,0.00008045763,0.0001071949],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007180408,"about_ca_system_score_gemma":0.000008427115,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003987493,"about_ca_topic_score_gemma":0.0008106127,"domain_scores_codex":[0.9992776,0.00006338768,0.0001406188,0.0002118035,0.0001180829,0.0001885045],"domain_scores_gemma":[0.9994418,0.00001286162,0.00005557957,0.0004446088,0.000006453986,0.00003871714],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0002300303,0.002250184,0.432402,0.00003828234,0.00005719979,0.000001612176,0.004338115,0.4727659,0.004263185,0.0778401,0.003731144,0.002082283],"study_design_scores_gemma":[0.00009772806,0.0001890846,0.8308722,0.000002329902,0.00001861432,0.000003999084,0.0002863094,0.1579676,0.0005322555,0.00973421,0.0002218689,0.0000738279],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9862878,0.000009336513,0.004947686,0.0003750903,0.0001013844,0.0004507486,8.380787e-7,0.00002944808,0.007797649],"genre_scores_gemma":[0.9989957,0.00000341948,0.0006671363,0.0000780175,0.00003950356,0.00008316385,0.000004158633,0.000006460278,0.0001224891],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3984703,"threshold_uncertainty_score":0.1663648,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04501332195156117,"score_gpt":0.2390017630468935,"score_spread":0.1939884410953324,"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."}}